Clutch sizes and hatching success of Canada geese nesting in Canterbury, New Zealand
Bibliographic record
Abstract
Nesting outcomes of Canada geese (Branta canadensis maxima) in Canterbury, New Zealand were recorded from a sedentary population nesting at coastal Lake Forsyth (1967–70) and from a seasonally migratory population nesting in headwater valleys of the Waimakariri River (1966–80). Mean clutch size in 462 Lake Forsyth nests was 5.3 (sd = 1.3) eggs, with clutches of 4, 5, and 6 eggs comprising 17%, 30% and 30% respectively of the total. Goslings hatched from 67.4% of 1,602 eggs in 298 monitored nests, and the entire clutch hatched successfully in 42.6% of the monitored nests. Mean productivity at hatching was 3.6 (sd = 2.3) goslings per nest. Mean clutch size in 1,211 Waimakariri River headwaters nests was 4.5 (sd = 1.3), with clutches of 4, 5, and 6 eggs comprising 25%, 32%, and 20% respectively of the total. Goslings hatched from 63.3% of 3,952 eggs in 871 monitored nests, and the entire clutch hatched successfully in 30.5% of the monitored nests. Mean productivity at hatching was 2.9 (sd = 1.9) goslings per nest. Relative to Canada geese in their native North American range, geese nesting at Lake Forsyth laid clutches of similar size, had similar hatching success but higher nest success whereas geese nesting in the Waimakariri River headwaters laid, on average, conspicuously smaller clutches, had similar hatching success, but higher nest success.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".