Bird research in the ACWERN lab at the Faculty of Forestry and Environmental Management, University of New Brunswick
Bibliographic record
Abstract
Research on forest bird ecology in the ACWERN (Atlantic Cooperative Wildlife Ecology Research Network) lab at the University of New Brunswick, Fredericton, since 1995 has focused on assessing the relative contributions of habitat quality at large (“landscape”) and small (“local” or “stand”) spatial scales. To do so we had to develop methods for assessing key demographic components of fitness (productivity and survival) at large spatial scales. The large extent of forest cover in the Maritimes contrasts with regions where such work has traditionally been carried out, in which forest is clearly fragmented by agriculture or residential development. Our main findings are that spatial effects in highly forested landscapes can often be detected only by using species-specific habitat models, rather than broader categories such as “mature” or “softwood”, that Blackburnian Warblers (Dendroica fusca) are effective indicators of mixedwood forest but define it differently than forest managers do, and that cavity nesters (e.g., woodpeckers) may require different habitat components for nesting and feeding and so cannot be managed for solely on the basis of providing snags for nesting. Our focus has shifted recently to intensive studies on a species at risk, Bicknell's Thrush (Catharus bicknelli), which in New Brunswick breeds in man-made regenerating softwood forest stands, and assessing its response both to precommercial thinning of the breeding habitat and to effects carrying over from the winter habitat in the Caribbean. Key words: landscape effects, thresholds, survival, productivity, fitness, carry-over, habitat, fragmentation
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.013 |
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".