Bibliographic record
Abstract
The meteorological mechanisms causing the recent increase in winter wind speed on Hudson Bay are investigated by examining the NARR dataset (The North American Regional Reanalysis Model) for the past several decades. Winter seasonal changes for atmospheric variables are examined and their interconnections are studied. Yearly mean near-surface temperatures are analyzed from 1948 to denote a rapid warming over Hudson Bay from late 1998 onwards. The surface albedo, air temperatures, mean sea level pressure and wind vector anomalies from 1998 to 2015 have also been studied. The comparison of the 1000 hPa wind vector mean and departures from 1981-2010 (normal period) averages have shown an intensification of anti-cyclonic anomaly pattern over most parts of Hudson Bay. The structure of the wind vector anomalies has revealed a contrast between cyclonic and anti-cyclonic local wind circulations mostly in the east, north and north-west regions along with wind speed increasing at 10 m, increases in near-surface air temperature and decreasing of the surface albedo. The anomalies of the wind vector analysing at different pressure levels show the change in wind direction mostly from northwesterly (zonal wind weakening) to south and easterlies. The polar jet wind vectors at 200 mb during anomaly time (1998-2015) have revealed the changes in magnitude and position. During winter anomaly time, the polar jet at 200 mb has been shifted mostly from rather mean colder north westerly currents to the rather warmer south and easterly anomaly currents over Hudson Bay areas. The yearly historical total accumulated Hudson Bay ice coverage during 1980-2015, using Canadian Ice Service data has shown a slight reduction in the north, north-west and eastern Hudson Bay. The linear regressions of the winter temperature anomaly at 2 m against albedo anomaly, alongside the wind speed anomaly at 10 m against air temperature at 2 m, have shown a relationship between these variables. Also, there is a statistically meaningful relation between decreased albedo and increased evaporation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".