Occurrence of the Connecticut Warbler Increases with Size of Patches of Coniferous Forest
Bibliographic record
Abstract
The Connecticut Warbler (Oporornis agilis) is a rare and declining neotropical migrant that breeds in the north-central United States and south-central Canada. To better understand the species' habitat needs, we analyzed 371 observations of the Connecticut Warbler over 18 years at 86 sites in 28 stands of forest in northern Minnesota. We considered the habitat and landscape at three spatial scales (buffer radii of 100, 500, and 1000 m) and regressed combinations of habitat variables with two response variables, the Connecticut Warbler's abundance (the total number of individuals ever recorded at a site or stand, with a zero-inflated negative binomial distribution) and frequency (the number of years recorded out of 18, with logistic regression). From a subset of models retained on the basis of Akaike's information criterion, we calculated model-averaged predictions for each combination of buffer size and response variable. Models based on Connecticut Warbler frequency at the 1000-m buffer performed best in comparisons of model-averaged predictions to observed data. At the 1000-m scale, Connecticut Warblers were positively associated with a combination of large patches of upland coniferous and lowland black spruce forest and were negatively associated with upland deciduous forest. From these models, we mapped predicted breeding habitat for the Connecticut Warbler in the areas sampled in northern Minnesota.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".