Species Associations and Habitat Influence the Range-Wide Distribution of Breeding Canada Geese (<i>Branta canadensis interior</i>) on Western Hudson Bay
Bibliographic record
Abstract
Inter- and intra-specific interactions are potentially important factors influencing the distribution of populations. Aerial survey data, collected during range-wide breeding population surveys for Eastern Prairie Population (EPP) Canada Geese (Branta canadensis interior), 1987–2008, were evaluated to assess factors influencing their nesting distribution. Specifically, associations between nesting Lesser Snow Geese (Chen caerulescens caerulescens) and EPP Canada Geese were quantified; and changes in the spatial distribution of EPP Canada Geese were identified. Mixed-effects Poisson regression models of EPP Canada Goose nest counts were evaluated within a cross-validation framework. The total count of EPP Canada Goose nests varied moderately among years between 1987 and 2008 with no long-term trend; however, the total count of nesting Lesser Snow Geese generally increased. Three models containing factors related to previous EPP Canada Goose nest density (representing recruitment), distance to Hudson Bay (representing brood-habitat), nesting habitat type, and Lesser Snow Goose nest density (inter-specific associations) were the most accurate, improving prediction accuracy by 45% when compared to intercept-only models. EPP Canada Goose nest density varied by habitat type, was negatively associated with distance to coastal brood-rearing areas, and suggested density-dependent intra-specific effects on recruitment. However, a non-linear relationship between Lesser Snow and EPP Canada Goose nest density suggests that as nesting Lesser Snow Geese increase, EPP Canada Geese locally decline and subsequently the spatial distribution of EPP Canada Geese on western Hudson Bay has changed.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".