Mayfield estimates versus apparent nest success in colonial geese
Bibliographic record
Abstract
ABSTRACT Unbiased estimates of nest survival are often required to make robust inference about population and habitat management. We studied nest survival of lesser snow (Anser caerulescens caerulescens) and Ross's (A. rossii) geese at Karrak Lake, Nunavut, in Canada's central Arctic, 1995–2012. We monitored nests throughout the nesting period, from early egg‐laying to late incubation, and revisited nests after predicted hatch dates to determine nest fate. Despite high nesting density and high nest visibility, detection of failed nests was lower than active nests; consequently, Mayfield nest success estimated with Program MARK was always lower than apparent nest success, the latter defined as the proportion of detected nests that produced ≥1 offspring. From data that included nests found after failure, however, annual nest survival estimated by Program MARK was related (r2 = 0.98 for both species) to apparent estimates, permitting accurate estimation of true nest success from apparent estimates. Nest survival probability (i.e., nest survival) in both species modeled with Program MARK varied annually (lesser snow geese = 0.234–0.795, Ross's geese = 0.273–0.901) and daily nest survival declined with nest age in most years. Within years, nests initiated later experienced lower survival for both lesser snow and Ross's geese (nest initiation date (NID), βNID = −0.068 [95% CI = −0.096, −0.041] and −0.082 [−0.119, −0.046], respectively). Nest survival was higher when days were relatively warm and dry for lesser snow geese (βdaily weather = 0.089 [0.054, 0.402]), but weather did not influence nest survival of Ross's geese. Disturbance by researchers had no influence on nest survival of either species. Sampling for contemporary estimators of nest survival that account for differential detection probabilities between active and inactive nests to produce unbiased estimates may not always be logistically feasible; thus, we urge researchers at least to derive predictive equations from a subset of nests specific to study sites and species to correct apparent estimates. © 2019 The Wildlife Society.
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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 | 0.003 |
| 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 | 0.000 | 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".