An evaluation of territory mapping to estimate fisher density
Bibliographic record
Abstract
We evaluated winter-territory mapping as a method for estimating fisher (Martes pennanti) density in a 210-km2 survey area in north-central Massachusetts in 1994 and 1995 by comparing estimates with simultaneous camera markresight estimates. Assuming intrasexual territoriality and accounting for all occupied habitat, territories of resident radio-marked fishers were mapped (mean = 54% of all territories in the study area), and those of unmarked resident fishers were identified from tracks and photographs. The total number indicated a population of 40 (19/100 km2) and 49 (23/100 km2) residents for 1994 and 1995, respectively. Results from replicated automatic-camera capturemarkresight surveys suggested slightly higher total numbers and densities of fishers in 1994 (44.5; 21/100 km2) and 1995 (52.9; 25/100 km2), but these estimates likely also included nonresident juveniles. Territory mapping and automatic-camera markresight methods resulted in very similar population estimates, but both require large numbers of radio-marked fishers to effectively detect small population changes (e.g., such as the 20% observed in this study). Individually marking animals would enhance markrecapture estimates.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".