Using the low-level aerial survey method to identify Marbled Murrelet nesting habitat
Bibliographic record
Abstract
Identifying and managing nesting habitat for the threatened Marbled Murrelet (Brachyramphus marmoratus) is difficult because it nests secretively, high in the canopies of large, old conifers of the Pacific Northwest. In British Columbia, low-level surveying from a helicopter is now a recommended standard method of assessing forested landscapes for key microhabitat features—such as availability of potential platforms and developed moss pads for nests, foliage cover above the nest, and accessibility—that are not distinguishable on air photos, satellite images, or forest cover maps. Using a sample of 111 nest sites and 139 random sites within forests greater than 140 years old and distributed across three study areas in south coastal British Columbia, we confirmed the effectiveness of the aerial survey method for classifying overall habitat quality of murrelet nesting habitat. The minimum map units were 3-ha patches. Overall, 40% of the 111 nest sites were in patches classed as Very High, 36% were in High, 15% were in Moderate, 6% were in Low, and 3% were in Very Low. Our ranking of habitat quality was most strongly influenced by estimates of platform availability and moss development. Using an information-theoretic approach, we identified that the Resource Selection Function scores of nest patches improved as elevation decreased, slope grade increased, and the proportion of emergent and canopy trees with mossy pads increased. We also confirmed that study area location affected the strength of model application. Our findings support the potential utility of the low-level aerial survey method for identifying or confirming Marbled Murrelet nesting habitat for land-management purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".