Alimentation d'urgence de cerfs de Virginie lors d'hivers rigoureux: Rabattage de tiges non commerciales versus distribution de moulée
Bibliographic record
Abstract
In northeastern regions of North America, deer sometimes face hard winters, which may kill more than 40 percent of the population. Management of their winter habitat is not enough to avoid extensive losses from starvation. Emergency feeding programs have therefore been developed to reduce population fluctuations, which make it difficult to manage the species. During the winters of 1996 and 1997, we simulated two emergency feeding programs for deer in two deer yards located in Bas Saint-Laurent, Quebec. One of the programs was linked to the cutting of stems of non-commercial species, and the other to the distribution of a specially adapted animal feed. In accordance to the regional intervention strategy, we supplied additional feed to satisfy about 50 percent of the deers' feed requirements. In this study, we have compared the costs of the two programs during four and eight week periods. In comparison to stem cuttings, feed distribution reduces by two to three times the expenses related to the program, while facilitating spatial distribution of the food. In conditions encountered in northeastern North America, feed distribution is definitely the most economically effective method of establishing an emergency feeding program for deer. Key words: costs, branch cutting, browsing, feed, emergency feeding, winter, deer yard, deer, Odocoileus virginianus
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".