Assessing <i>Catharus bicknelli</i> (Bicknell’s Thrush) habitat dynamics: A high-resolution model based on LiDAR metrics
Bibliographic record
Abstract
Abstract Assessing species occurrences can be challenging due to issues such as species detectability and difficulties in accessing habitats for surveys. These limitations hinder the ability to adequately inform conservation or management decisions, particularly for cryptic species, which can be partially addressed with high-resolution, spatial explicit models. However, high-resolution, spatially explicit models projecting species can help resolve these challenges. We modeled the probability of occurrence of Catharus bicknelli (Bicknell’s Thrush), globally vulnerable species, in southern Québec (Canada) using LiDAR-derived metrics. The model was calibrated with 139 occurrences (with 10-m spatial accuracy), each paired with 10 random locations within a 523–1,569 m buffer, and validated with an independent dataset of 3,928 point counts. Based on model output, we applied 2 statistical thresholds to guide decision-making. The probability of occurrence of the species was highest in dense, low-canopy balsam fir stands at high elevation. The top-ranked model also included forest succession, with an adjustment to canopy height to account for tree growth since the LiDAR data were acquired. This modeling approach provides a valuable tool for tracking the spatio-temporal dynamics of C. bicknelli habitat and informing more effective conservation strategies for this species at risk in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".