Abundance and Distribution of Harlequin Ducks in the Hudson and James Bay Area, Québec
Bibliographic record
Abstract
As part of the Hydro-Québec Grande-Baleine (Great Whale) hydroelectric project feasibility studies, Harlequin Duck (Histrionicus histrionicus) surveys were conducted in 1990 and 1991 in the eastern Hudson Bay and James Bay drainage basins. A total of 142 and 420 Harlequin Ducks were counted in 1991 and 1992, respectively, of which 142 (1991) and 356 (1992) were found in the area surveyed both years. Most individuals were in pairs and the overall sex-ratio did not deviate significantly from 1:1. The highest numbers of Harlequin Ducks counted over the two years were found on the Little Whale River, Des Loups-Marins Lake, and Nastapoka, À l'Eau Claire and Boutin Rivers. Highest pair densities were observed in June 1992 on rivers located in tundra and forest tundra i.e., the lower Little Whale, À l'Eau Claire and Nastapoka Rivers, and near D'Iberville Lake. In 1992, pair densities varied between 0.003 and 0.093 pair/km, depending on the watershed, and followed a latitudinal gradient. Two broods were located in 1991 and three were found during a preliminary survey conducted in 1989. Broods were located on Boutin, Nastapoka, and Great Whale Rivers, as well as along the Hudson Bay coast. The difference in the number of Harlequin Ducks found in June 1991 and 1992 may have been related to weather and methodological factors. Considering the vastness of northern Québec and the limited area surveyed during this study, we suggest that Harlequin Ducks breeding in northern Québec may well number in the thousands, and represent a very high proportion of the Greenland molting and wintering populations.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".