Bibliographic record
Abstract
Of the 83 Christmas Bird Counts (CBC) conducted last winter, almost all, 81, were accompanied by a Mammal Count -an increase of two over last year.Only 2,884 mammals were counted, however, compared to the previous winter's 4,937.Variety was down, as well, with only 29 species heard or seen on count day compared to last year's 34.Most of the decline in numbers was due to a decrease in deer.White-tailed Deer dropped from 1,880 in 2022-23 to 610 individuals this past winter; Mule Deer from 1,379 to 586 animals.These changes are probably mainly due to a lack of snow, which did not force animals in to towns and farmsteads in search of food.Also, deer would be much less conspicuous on a snowless background.Due to the unusually warm weather, Richardson's Ground-Squirrels were much in evidence.Twenty-five were seen on seven counts.None are recorded on most annual counts.White-tailed Jackrabbits continue to prosper in Regina with a new provincial high of 218 animals.Unfortunately only 74 hares were seen in the rest of the province.A Wolverine was seen during the count period (24 December) near the E.B. Campbell Dam.This is an astonishing record as the only other Christmas Mammal Count (CMC) record was on Nisbet Forest West Count on 26 December 2022!Most unwanted were Wild Boar tracks seen during the count period at Archerwill (the animal was actually seen in November).The only previous record was of tracks seen on the Pike Lake count on 5 January 2019.Indian Head had the most species seen or heard with 13, Odessa was the runner-up with 12 species.No new species were added this past winter.The all-time provincial total of species seen or heard remains at 52, plus three species found dead and three others recorded only on the basis of tracks.For information on participants, weather, coverage and location of CMCs see the CBC summary in this issue.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.113 | 0.035 |
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".