DO CROSS-SCALE CORRELATIONS CONFOUND ANALYSIS OF NEST SITE SELECTION FOR CHESTNUT-BACKED CHICKADEES?
Bibliographic record
Abstract
Multiscale models of nest site selection often ignore cross-scale correlations (correlations between predictor variables at different scales). We reexamined nest site selection of Chestnut-backed Chickadees (Poecile rufescens) at two scales within territories to isolate: (1) variation associated purely with variables measured at the patch (0.031 ha) and tree scales, and (2) variation shared by variables measured at the patch and tree scales. We used conditional (fixed-effect) logistic regression to build a patch, tree, and full model, and subtracted pure and shared components of variation from the deviance explained by the full model. Tree scale and patch scale variables accounted for 85% and 9% of the total explained variation, respectively. Only 6% of explained variation in nest site locations was due to cross-scale correlations. We suggest that multiscale habitat selection studies incorporate a diagnostic tool like variance decomposition to avoid spurious results caused by lack of independence of habitat relationships.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".