Model selection with overdispersed distance sampling data
Bibliographic record
Abstract
Abstract Distance sampling (DS) is a widely used framework for estimating animal abundance.DSmodels assume that observations of distances to animals are independent. Non‐independent observations introduce overdispersion, causing model selection criteria such asAICorAICcto favour overly complex models, with adverse effects on accuracy and precision. We describe, and evaluate via simulation and with real data, estimators of an overdispersion factor ( ), and associated adjusted model selection criteria (QAIC) for use with overdispersedDSdata. In other contexts, a single value of is calculated from the “global” model, that is the most highly parameterised model in the candidate set, and used to calculateQAICfor all models in the set; the resultingQAICvalues, and associated ΔQAICvalues andQAICweights, are comparable across the entire set. Candidate models of theDSdetection function include models with different general forms (e.g. half‐normal, hazard rate, uniform), so it may not be possible to identify a single global model. We therefore propose a two‐step model selection procedure by whichQAICis used to select among models with the same general form, and then a goodness‐of‐fit statistic is used to select among models with different forms. A drawback of this approach is thatQAICvalues are not comparable across all models in the candidate set. Relative toAIC,QAICand the two‐step model selection procedure avoided overfitting and improved the accuracy and precision of densities estimated from simulated data. When applied to six real datasets, adjusted criteria and procedures selected either the same model asAICor a model that yielded a more accurate density estimate in five cases, and a model that yielded a less accurate estimate in one case. ManyDSsurveys yield overdispersed data, including cue counting surveys of songbirds and cetaceans, surveys of social species including primates, and camera‐trapping surveys. Methods that adjust for overdispersion during the model selection stage ofDSanalyses therefore address a conspicuous gap in theDSanalytical framework as applied to species of conservation concern.
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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 | 0.026 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".