Does species composition of arctic geese recovered in prairie Canada vary by hunter residency?
Bibliographic record
Abstract
Abstract Estimates of goose harvest by the National Harvest Survey (NHS) in Canada are based on the assumption that species composition of harvest by non‐Canadians that hunt in Canada is equivalent to that of Canadians. Non‐Canadian hunters are not sampled for composition of species harvested, so differences in proportions harvested per hunter could lead to biased harvest estimates; bias would increase with increasing proportions of unsampled non‐Canadians in relation to sampled Canadian hunters. My objective was to test the assumption of equality of species composition between these 2 strata of hunters for Alberta, Saskatchewan, and Manitoba using recoveries of cackling ( Branta hutchinsii ), Ross's ( Chen rossii ), lesser snow ( C. caerulescens caerulescens ), and greater white‐fronted ( Anser albifrons ) geese marked south of Queen Maud Gulf in Nunavut, Canada's Central Arctic. I used multinomial logistic regression of recoveries between Canadian and non‐Canadian hunters controlling for hunting season. Non‐Canadian hunters selected Ross's geese over white‐fronted geese compared to Canadians hunting in both Alberta and Saskatchewan, where all 4 species are harvested. Thus, harvest estimates for Ross's geese may be biased low in Alberta and Saskatchewan and those for white‐fronted geese biased high. Waterfowl species harvested by non‐Canadians could be sampled to estimate and correct biases in the NHS. © 2011 The Wildlife Society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".