The Reliability and Application of Methods Used to Predict Suitable Nesting Habitat for Marbled Murrelets
Bibliographic record
Abstract
Identifying and mapping suitable nesting habitat within coastal forests is a key element in the recovery and management of the Marbled Murrelet (Brachyramphus marmoratus), which is listed as Threatened in Canada. This article reviews the reliability and application of three primary methods used to assess habitat suitability: the BC Model, a GIS-based algorithm using Vegetation Resources Inventory (VRI); air photo interpretation (API), direct assessments from air photos based on forest structure; and low-level aerial surveys (LLAS), helicopter surveys assessing forest canopy structure and the presence of potential nest platforms. In general, LLAS provides the most reliable identification and is the only method of the three that estimates the occurrence of potential nest platforms in the forest canopy. The other two methods, API and the BC Model, are substantially less reliable in identifying habitat actually used by nesting murrelets. Spatial scale and survey intensity affect habitat classification using all three methods. Generally, fine-scale (~3 ha), high-intensity classifications with LLAS and API are more likely to detect suitable habitat at known nest sites than those using medium-scale (10s or 100s ha) and/or low-intensity classifications. Even with fine-scale high-intensity application, 15% and 25% of known nest sites were still classified as “unsuitable” habitat with LLAS and API, respectively. All three methods applied at the medium scale for mapping appeared to miss fine-scale nesting habitat (i.e., small numbers of suitable trees occurring in otherwise unsuitable habitat). Areas of mapped suitable habitat can therefore be adjusted to take this discrepancy into account, and methods to do this are discussed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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 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".