SYNOPTIC AND SURFACE CLIMATOLOGY INTERACTIONS IN THE CENTRAL CANADIAN SUBARCTIC: NORMAL AND EL NIÑO SEASONS
Bibliographic record
Abstract
An objective hybrid classification of daily surface weather maps for Churchill, Manitoba, was used to determine the dominant synoptic conditions during the snowmelt and snow-free periods for the Canadian central subarctic. This classification yielded different dominant synoptic types for the two normal and one El Niño (EN) seasons. The analysis produced seven dominant synoptic types for the study location during the pre-growing and growing periods (April 20 to September 7), accounting for approximately 90% of the days in the study period, during the normal seasons (1996 and 1997). However, during the EN season (1998), the classification yielded eight dominant synoptic types, also accounting for approximately 90% of the days in the study period. The effects of source regions were used to explain the observed air mass characteristics, and their influence on the study location. Cooler, drier air masses were the most frequent at the study location during the normal seasons. Arctic high pressure systems approaching from the northwest and stationary high pressure systems to the south brought the coolest and warmest conditions, respectively. During the EN season, there was a change in the frequency distribution of the dominant synoptic types over the pre-growing and growing periods. This frequency distribution in conjunction with the change in source region characteristics caused by the EN influence proved to be important to the influx of moisture to the region during the pre-growing period. These synoptic regimes and source region interactions exerted strong controls on the precipitation and evaporation components of the water balance during the normal and EN seasons as observed in terms of cloud cover, radiation, and precipitation and evaporation efficiencies. [Key words: synoptic climatology, subarctic, evaporation, El Niño (EN).]
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".