Frontal Processes in the Saint Lawrence River Valley During the WINTRE-MIX Field Program
Bibliographic record
Abstract
The Saint Lawrence River Valley (SLRV) and Lake Champlain Valley (LCV) frontis a key factor influencing the weather of southern Quebec, southeastern Ontario, andportions of northern Vermont and New York. In this study, an analysis of SLRV-LCV frontal processes is performed using data retrieved during intensive observation periods (IOPs) of the WINTRE-MIX (WINter precipitation Type REsearch MultI-scale eXperiment) field campaign, occurring from 1 February through 15 March 2022. In particular, case studies selected from the Intensive Observation Periods during the WINTRE-MIX field campaign are presented. These case-study analyses consist of a unique synthesis of high-resolution WINTRE-MIX observations and ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalyses v5 (ERA5). As a result, the author gains new insight into the synoptic-scale and mesoscale environments of frontal processes occurring in the SLRV and LCV regions. Key frontal features are found within the SLRV and LCV frontal case studies that have been shown in previous research, such as a frontal strength that is maximized near the surface, vertical motions found directly above the low-level frontogenesis, high static stability in the frontal zone, and the presence of strong vertical wind shear. One particular SLRV and LCV front case study is hypothesized to illustrate features of SLRV frontogenetical modulation of precipitation
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".