Explore the vast range of titles available.

We quantify the biases in the diurnal cycle of air temperature in ERA5, using hourly climate station data for four stations in Saskatchewan, Canada. Compared with ERA-Interim, the biases in ERA5 have been greatly reduced, and show no differences with snow cover. We compute fits to the ERA5 mean air temperature biases based on ERA5 effective cloud albedo. They can be used to improve the ERA5 diurnal cycle of air temperature for modeling agricultural processes. Diurnally, ERA5 has a negative wind speed bias, which increases quasi-linearly with wind speed, and is greater in the daytime than at night. We evaluate ERA5 precipitation against the original climate station precipitation data, and a second generation adjusted precipitation dataset by Mekis and Vincent [2011]. For the warm season, ERA5 has a high bias of 8±9% above the Mekis dataset. ERA5 is -22±7% below the Mekis estimate in winter, suggesting that their correction with snow may be too large. It is likely that the ERA5 precipitation bias is small, which is encouraging for agricultural modelling. Data from a BSRN site near Regina shows that the biases in the downwelling shortwave and longwave radiation estimates in ERA5 are small, and have changed little from ERA-Interim. We showed that the annual cycle of the Saskatchewan surface energy and water budgets in ERA5 are realistic. In particular the damping of extremes in summer precipitation by the extraction of soil water is comparable in ERA5 to our earlier observational estimate based on gravity satellite data.

It is now widely agreed that a positive affective state is a crucial component of animal well-being. The judgment bias test represents a widespread tool used to assess animals' optimistic/pessimistic attitude and to evaluate their emotional state and welfare. Judgment bias tests have been used several times with dogs (Canis familiaris), in most cases using a spatial test with a bowl placed in ambiguous positions located between a relatively positive trained location (P) which contains a baited bowl and a relatively negative trained location (N) which contains an empty bowl. The latency to approach the bowl in the ambiguous locations is an indicator of the dog's expectation of a positive/negative outcome. However, results from such tests are often inconclusive. For the present study, the judgment bias test performance of 51 shelter dogs and 40 pet dogs was thoroughly analysed. A pattern emerged with shelter dogs behaving in a more pessimistic-like way than pet dogs. However, this difference between the two populations was detected only when analysing the raw latencies to reach the locations and not the more commonly applied adjusted score (i.e. average latency values). Furthermore, several methodological caveats were found. First of all, a non-negligible percentage of dogs did not pass the training phase, possibly due to the experimental paradigm not being fully suited for this species. Second, results showed a high intra-dog variability in response to the trained locations, i.e. the dogs' responses were not consistent throughout the test, suggesting that animals may not have fully learned the association between locations and their outcomes. Third, dogs did not always behave differently towards adjacent locations, raising doubts about the animals' ability to discriminate between locations. Finally, a potential influence of the researcher's presence on dogs' performance emerged from analyses. The implications of these findings and potential solutions are discussed.

With the modernization of GPS and the establishment of additional global navigation satellite system (GNSS) constellations, such as Galileo, Beidou, and QZSS, more and more GNSS satellites are available transmitting on various frequencies with multiple signal modulations. In order to cope with the increasing number of observation types, the commonly used differential approach becomes more and more difficult regarding book-keeping. The actually processed original observation types have to be known in advance to define a linearly independent set of differential signal biases (DSB) while processing GNSS data. An alternative treatment of code biases is the usage of observable-specific signal biases (OSB) where the setup and correction of biases become trivial. Potential dependencies of the bias parameters can be considered after the setup of normal equations (NEQs), e.g., immediately before it is inverted. The code bias results are retrieved on a daily basis and their NEQs stored. This allows to combine bias results from various sources (or analysis lines) and different time periods. By combining all daily bias NEQs, we have generated a coherent multi-year bias solution from 2000 to 2017 with one common datum. If absolute receiver calibrations are available, the multi-year solution could be aligned to those receivers and thus could lead to an absolute estimation of the code biases. Finally, the estimated satellite OSBs are used for the receiver compatibility grouping testing which receivers are compatible with which bias sets. This may be achieved by solving for so-called OSB multipliers.

It is foreseeable that the BeiDou navigation satellite system with global coverage (BDS-3) and the BeiDou navigation satellite (regional) system (BDS-2) will coexist in the next decade. Care should be taken to minimize the adverse impact of the receiver-related biases, including inter-system biases (ISBs), differential code biases (DCB), and differential phase biases (DPB) on the positioning, navigation, and timing (PNT) provided by global navigation satellite systems (GNSS). Therefore, it is important to ascertain the intrinsic characteristics of receiver-related biases, especially in the context of the combination of BDS-3 and BDS-2, which have some differences in their signal level. We present a method that enables time-wise retrieval of between-receiver ISBs, DCB, and DPB from multi-frequency multi-GNSS observations. With this method, the time-wise estimates of the receiver-related biases between BDS-3 and BDS-2 are determined using all five frequencies available in different receiver pairs. Three major findings are suggested based on our test results. First, code ISBs are significant on the two overlapping frequencies B1II and B2b/B2I between BDS-3 and BDS-2 for a baseline with non-identical receiver pairs, which disrupts the compatibility of the two constellations. Second, epoch-wise DCB estimates of the same type in BDS-3 and BDS-2 can show noticeable differences. Thus, it is unreasonable to treat them as one constellation in PNT applications. Third, the DPB of BDS-3 and BDS-2 may have significant short-term variations, which can be attributed to, on the one hand, receivers composing baselines, and on the other hand, frequencies.

Personality traits are patterns of thought, emotion, and behavior that are relatively consistent over time and across situations. Judging the traits of others and of oneself is a ubiquitous and consequential activity of daily life, which raises two important questions. First, how does accurate personality judgment happen? According to the Realistic Accuracy Model (RAM), accuracy in such judgments is achieved when relevant behavioral information is available to and detected by a judge who then utilizes that information correctly. Second, when are personality judgments accurate? The RAM identifies four principal moderators of accurate personality judgment, which are properties of the target of judgment, the trait that is judged, the information upon which the judgment is based (i.e., its quantity and quality), and the individual making the judgment. People usually manage to make personality judgments that are accurate enough for navigation of the complex social world; research on accuracy seeks to understand how and when this happens.

Although some have heralded recent political and cultural developments as signaling the arrival of a postracial era in America, several legal and social controversies regarding \"reverse racism\" highlight Whites' increasing concern about anti-White bias. We show that this emerging belief reflects Whites' view of racism as a zero-sum game, such that decreases in perceived bias against Blacks over the past six decades are associated with increases in perceived bias against Whites—a relationship not observed in Blacks' perceptions. Moreover, these changes in Whites' conceptions of racism are extreme enough that Whites have now come to view anti-White bias as a bigger societal problem than anti-Black bias.

Modeling human cognition is challenging because there are infinitely many mechanisms that can generate any given observation. Some researchers address this by constraining the hypothesis space through assumptions about what the human mind can and cannot do, while others constrain it through principles of rationality and adaptation. Recent work in economics, psychology, neuroscience, and linguistics has begun to integrate both approaches by augmenting rational models with cognitive constraints, incorporating rational principles into cognitive architectures, and applying optimality principles to understanding neural representations. We identify the rational use of limited resources as a unifying principle underlying these diverse approaches, expressing it in a new cognitive modeling paradigm called resource-rational analysis . The integration of rational principles with realistic cognitive constraints makes resource-rational analysis a promising framework for reverse-engineering cognitive mechanisms and representations. It has already shed new light on the debate about human rationality and can be leveraged to revisit classic questions of cognitive psychology within a principled computational framework. We demonstrate that resource-rational models can reconcile the mind's most impressive cognitive skills with people's ostensive irrationality. Resource-rational analysis also provides a new way to connect psychological theory more deeply with artificial intelligence, economics, neuroscience, and linguistics.

Disagreements over whether polarization exists in the mass public have confounded two separate types of polarization. When social polarization is separated from issue position polarization, both sides of the polarization debate can be simultaneously correct. Social polarization, characterized by increased levels of partisan bias, activism, and anger, is increasing, driven by partisan identity and political identity alignment, and does not require the same magnitude of issue position polarization. The partisan-ideological sorting that has occurred in recent decades has caused the nation as a whole to hold more aligned political identities, which has strengthened partisan identity and the activism, bias, and anger that result from strong identities, even though issue positions have not undergone the same degree of polarization. The result is a nation that agrees on many things but is bitterly divided nonetheless. An examination of ANES data finds strong support for these hypotheses.

The intra-system biases, including differential code biases (DCBs) and differential phase biases (DPBs), are generally defined as the receiver-dependent hardware delays between different frequencies in a single global navigation satellite system (GNSS) constellation. Likewise, the inter-system biases (ISBs) are the differential code and phase hardware delays between different GNSSs, which are of great relevance for combined processing of multi-GNSS and multi-frequency observations. Although the two biases are usually assumed to remain unchanged for at least 1 day, they sometimes can exhibit remarkable intraday variability, likely due to environmental factors, particularly the ambient temperature. It has been proved that the possible short-term temporal variations of receiver DCBs and DPBs are directly related to ambient temperature fluctuation. We analyze whether the variability of the biases is sensitive to temperature and further identify how this affects the performance of real-time kinematic (RTK) positioning. Our numerical tests, carried out using GPS, BDS-3, Galileo and QZSS observations collected by zero and short baselines, suggest two major findings. First, we found that while ISBs associated with overlapping frequencies are fairly stable, those associated with non-overlapping frequencies can exhibit remarkable variability over a rather short period of time, driven by the changes of ambient temperature. Second, by pre-calibrating and modeling of the biases for the baselines at hand, the empirical success rates and positioning performance can be significantly improved when compared to classical and inter-system differencing, with both models assuming time-invariant receiver DCBs, DPBs and ISBs.

Although evidence-based algorithms consistently outperform human forecasters, people often fail to use them after learning that they are imperfect, a phenomenon known as algorithm aversion . In this paper, we present three studies investigating how to reduce algorithm aversion. In incentivized forecasting tasks, participants chose between using their own forecasts or those of an algorithm that was built by experts. Participants were considerably more likely to choose to use an imperfect algorithm when they could modify its forecasts, and they performed better as a result. Notably, the preference for modifiable algorithms held even when participants were severely restricted in the modifications they could make (Studies 1–3). In fact, our results suggest that participants’ preference for modifiable algorithms was indicative of a desire for some control over the forecasting outcome, and not for a desire for greater control over the forecasting outcome, as participants’ preference for modifiable algorithms was relatively insensitive to the magnitude of the modifications they were able to make (Study 2). Additionally, we found that giving participants the freedom to modify an imperfect algorithm made them feel more satisfied with the forecasting process, more likely to believe that the algorithm was superior, and more likely to choose to use an algorithm to make subsequent forecasts (Study 3). This research suggests that one can reduce algorithm aversion by giving people some control—even a slight amount—over an imperfect algorithm’s forecast. Data, as supplemental material, are available at https://doi.org/10.1287/mnsc.2016.2643 . This paper was accepted by Yuval Rottenstreich, judgment and decision making .