Peer Review #3 of "Integrated population modelling reveals potential drivers of demography from partially aligned data: a case study of snowy plover declines under human stressors (v0.2)"
Bibliographic record
Abstract
Knowledge of demography is essential for understanding wildlife population dynamics and developing appropriate conservation plans.However, population survey and demographic data (e.g., capture-recapture) are not always aligned in space and time, hindering our ability to robustly estimate population size and demographic processes.Integrated population models (IPMs) can provide inference for population dynamics with poorly aligned but jointly analysed population and demographic data.In this study, we used an IPM to analyse partially aligned population and demographic data of a migratory shorebird species, the snowy plover (Charadrius nivosus).Snowy plover populations have declined dramatically during the last two decades, yet the demographic mechanisms and environmental drivers of these declines remain poorly understood, hindering development of appropriate conservation strategies.We analysed 21 years of partially aligned population survey, nest survey, and capture-recapture-resight data in three snowy plover populations (i.e., Texas, New Mexico, Oklahoma) in the Southern Great Plains of the US.By using IPMs we aimed to achieve better precision while evaluating the effects of wetland habitat and climatic factors (minimum temperature, wind speed) on snowy plover demography.Our IPM provided reasonable precision for productivity measures even with missing data, but population and survival estimates had greater uncertainty in years without corresponding data.Our model also uncovered the complex relationships between wetland habitat, climate, and demography with reasonable precision.Wetland habitat had positive effects on snowy plover productivity (i.e., clutch size and clutch fate), indicating the importance of protecting wetland habitat under climate change and other human stressors for the conservation of this species.We also found a positive effect of minimum temperature on snowy plover productivity, indicating potential benefits of warmth during
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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 | 0.011 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.225 | 0.092 |
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".