Martens and fishers (Martes) in human-altered environments: an international perspective
Bibliographic record
Abstract
Is Mustelid Life History Different?- World Distribution and Status of the Genus Martes in 2000.- Geographical and Seasonal Variation in Food Habits and Prey Size of European Pine Martens.- Territoriality and Home-Range Fidelity of American Martens in Relation to Timber Harvesting and Trapping.- Martes Foot-Loading and Snowfall Patterns in Eastern North America: Implications to Broad-Scale Distributions and Interactions of Mesocarnivores.- Home Ranges, Cognitive Maps, Habitat Models and Fitness Landscapes for Martes.- Relationships Between Stone Martens, Genets and Cork Oak Woodlands in Portugal.- Relationships Between Forest Structure and Habitat Use by American Martens in Maine, USA.- Effect of Ambient Temperature on the Selection of Rest Structures by Fishers.- Zoogeography, Spacing Patterns, and Dispersal in Fishers: Insights Gained from Combining Field and Genetic Data.- Harvest Status, Reproduction and Mortality in a Population of American Martens in Quebec, Canada.- Are Scat Surveys a Reliable Method for Assessing Distribution and Population Status of Pine Martens?- Postnatal Growth and Development in Fishers.- Field Anesthesia of American Martens Using Isoflurane.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".