Tree cavity nest productivity datasets for British Columbia (Canada) and Misiones (Argentina), 1995-2018
Bibliographic record
Abstract
Datasets used to examine the regional and local factors influencing lifetime productivity (number and richness of occupants) of individual tree cavities across two divergent forest ecosystems: temperate mixed forest in Canada and subtropical Atlantic Forest, Argentina (Cockle et al 2019, Lifetime productivity of tree cavities used by cavity-nesting animals in temperate and subtropical forests, Ecological Applications). We monitored and measured nesting cavities used by birds and mammals over 22 breeding seasons (1995–2016) in Canada and 12 breeding seasons (2006–2017) in Argentina. The data included here support the key findings of our paper: (1) Species richness within cavities increased with number of nesting events at similar rates in Canada and Argentina, in both cases much slower than expected if within-cavity species assemblages were random. (2) Lifetime cavity productivity was strongly related to the cavity’s lifespan, and only weakly or inconsistently related to cavity characteristics selected by individuals in short-term nest-site selection studies. (3) Species turnover among (vs. within) cavities explained most cavity-nester diversity at the landscape scale. (4) in Canada, species accumulation was fastest when sampling across high-use cavities; in Argentina, rates of species accumulation were similar across high- and low-use cavities, and fastest when both high- and low-use cavities were pooled.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".