Social Indices of Breeding Productivity in Parkland Mallards
Bibliographic record
Abstract
Abstract Social indices were developed to assess breeding productivity of waterfowl based on weekly roadside surveys of social groupings (i.e., pairs, lone M, flocked M). We calculated social indices for mallard ( Anas platyrhynchos ) populations breeding on 16 study sites in the Canadian parklands from 1993 to 1998 using 7 previously developed indices. We also calculated duckling:pair ratios from our roadside counts, and we obtained independent measures of nesting effort, nesting success, female success, and fledging rate for these same 16 sites from a concurrent telemetry study. Social indices were correlated ( r 2 = 0.28‐0.67) with telemetry‐based measures of breeding productivity in 5 of 7 cases, with the strongest relationships deriving from indices that emphasized renesting effort. The 2 ineffective social indices ( r 2 ≤ 0.13) both measured early onset of nesting activity. Duckling:pair ratios could be calculated more easily from the same survey data and also were correlated ( r 2 = 0.26‐0.48) with measures of breeding productivity. Because surveys measuring late‐nesting effort also can enumerate early hatched ducklings, we recommend that waterfowl researchers use duckling:pair ratios rather than social indices because ducking:pair ratios are more easily interpretable. Development of sightability‐adjustment factors for pair and duckling surveys could further enhance the utility of duckling:pair ratios as indices of breeding productivity in mallards.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".