Comparing the predictive capability of forest songbird habitat models based on remotely sensed versus ground-based vegetation information
Bibliographic record
Abstract
Habitat suitability models allow for predictive modeling of the supply of wildlife habitat through time under various forest harvesting scenarios. These models often rely on remotely sensed data in a forest resource inventory (FRI). However, the level of detail and (or) the accuracy of a FRI may be insufficient to accurately predict habitat suitability for forest birds. We tested if detailed vegetation measurements created habitat suitability models with better predictive power than models that used FRI data alone and if rough estimates of shrub cover were sufficient to supplement FRI data to create models with similar predictive power. For 28 species of forest birds, we found that less of the variation in abundance and (or) occurrence (% deviance explained) could be explained by models using FRI data alone (34% ± 2%) than by models using detailed vegetation information (40% ± 2%). However, when shrub density rank was included with FRI data, we found no difference in the deviance explained by the two model sets (39% ± 2% vs. 40% ± 2%). The best-fitting models containing the same vegetation parameters but using different methods of vegetation sampling were similar. These results suggest that coarse habitat classification schemes may be as effective in describing the major variance in bird community structure in the boreal forest as detailed vegetation inventory data.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".