Using patch and landscape variables to model bird abundance in a naturally heterogeneous landscape
Bibliographic record
Abstract
Regression models were developed to predict relative bird abundance in a naturally heterogeneous landscape using patch and landscape spatial scales. Breeding birds were surveyed with point counts on 140 study sites in 1997 and 1998. Aerial photographs were digitized to obtain habitat patch information, such as area, shape, and edge contrast. Classified remote-sensing data were gathered to provide information on landscape composition and configuration within a 1-km 2 area around the study sites. Stepwise multiple linear regression was used to develop 40 species-specific models within specific habitat types using patch and landscape characteristics. In 38 out of the 40 models, area of the habitat patch was first selected as the most important predictor of relative bird abundance. Variables related to the landscape were retained in 6 of the 40 models. In this naturally heterogeneous region, the landscape surrounding the patch contributed little to explaining relative bird abundance. The models were evaluated by examining how well they predicted relative bird abundance in a test set not included in the original analyses. The results of the test data were reasonable: >79% of the test observations were within the prediction intervals established by the training data.
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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 | 0.000 |
| 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".