Wolf, <em>Canis lupus</em>, Behavior in Areas of Frequent Human Activity
Bibliographic record
Abstract
We report incidental observations of Wolves (Canis lupus) tolerating human activity in central Wisconsin. Three monitored packs raised pups in close proximity to varying levels of human activity. Wolf pups were raised <350m from rearing pens of the endangered Whooping Crane (Grus americana), which saw daily human activity. One pack used cornfields as rendezvous sites within 175 m of a maintenance shed visited regularly by workers. Another pack centered their activities along a well-traveled state highway using both the verge and the road center for activity. Aerial locations of 10 yearling and adult dispersing Wolves were plotted to evalute human densities in natal territories relative to dispersal and post-dispersal territories. Township densities (mean = 9.02 humans/km2, SE = 4.015) and residential densities (mean = 5.59 housing units/km2, SE = 2.12 ) in natal pack territories were significantly greater (P <.01) for dispersal and post-dispersal township densities (mean = 43.98 humans/km2, SE =7.37) and residence densities (mean = 23.12 housing units/km2, SE =3.49). Furthermore, a pup negotiated the densely populated region of northern Illinois and dispersed from central Wisconsin to east-central Indiana, a distance of at least 690 km. As Wolves live in closer proximity to humans, living in areas of higher township and residential densities, they can be expected to be more habituated to people, increasing the probability of human/Wolf conflicts.
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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 | 0.000 | 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) | 0.001 | 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".