Principles and interest of GOF tests for multistate captureârecapture models
Bibliographic record
Abstract
Optimal goodness–of–fit procedures for multistate models are new. Drawing a parallel with the corresponding single–state procedures, we present their singularities and show how the overall test can be decomposed into interpretable components. All theoretical developments are illustrated with an application to the now classical study of movements of Canada geese between wintering sites. Through this application, we exemplify how the interpretable components give insight into the data, leading eventually to the choice of an appropriate general model but also sometimes to the invalidation of the multistate models as a whole. The method for computing a corrective overdispersion factor is then mentioned. We also take the opportunity to try to demystify some statistical notions like that of Minimal Sufficient Statistics by introducing them intuitively. We conclude that these tests should be considered an important part of the analysis itself, contributing in ways that the parametric modelling cannot always do to the understanding of the data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.087 | 0.256 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".