Effectiveness of Ecological Units for Stratification of Bird Habitat in Yukon-Charley Rivers National Preserve, Alaska
Bibliographic record
Abstract
For a comprehensive bird inventory of Yukon-Charley Rivers National Preserve, Alaska, we stratified the 1 million hectare study area by large, physiographically defined regions known as ecological units. Point-count data from the bird inventory were used to test the ability of the ecological units to differentiate bird assemblages, and compare the effectiveness of ecological units to fine-scale vegetation types. The ecological units were a synthesis of geology, landforms, soils, and vegetation mapped at a scale of 1:250,000; the vegetation types were based on vegetation within 50 m of the sample points. Nonparametric multivariate statistical tests showed that ecological units and vegetation types had similar success in differentiating bird assemblages, despite their different scales and conceptual bases. Analyses of individual bird species showed that both ecological units and vegetation types provide useful and complementary information about bird habitat selection. Ecological units have several advantages over vegetation types as sample-area strata: they are stable over time, logistically easier to sample in a large roadless study area, and they allow one to obtain a larger bird sample size through inclusion of birds detected at greater distances.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".