Bibliographic record
Abstract
Of the 78 Christmas Bird Counts conducted last winter, 73 were accompanied by a Mammal Count -a decrease from last year's 81.The drop was at least partly responsible for a decline in the number of mammals seen or heard, from 4,512 to 3,664.White-tailed Deer, with 1,589 individuals, and Mule Deer, with 778 animals, were by far the most commonly encountered mammals.Another ungulate, the Pronghorn, with 534 animals, took third place, a position usually held by the Coyote.In fact, the Coyote, with only 108 animals, fell to fifth place behind the White-tailed Jackrabbit and Eastern Fox Squirrel.Almost half (77) of the 159 jackrabbits were, however, on the Regina count.The ever-expanding Eastern Fox Squirrel numbered 141 animals.One wonders how the 36 Eastern Gray Squirrels in Swift Current will fare if and when the "competition" arrives from the east?On the other end of the spectrum, rarities included a "should be hibernating" Least Chipmunk on 16 December at Fort Qu'Appelle, and a Cougar in Cypress Hills Provincial Park on 30 December.Odessa had the most species seen or heard with 13, and the runner-up was Craven with 11.No new species were added this past winter so the all-time provincial total remains at 51 species seen or heard (plus three species found dead and three others recorded only on the basis of tracks).For information on participants, weather, coverage and location of the Christmas Mammal Counts, see the Christmas Bird Count 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.103 | 0.031 |
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".