Bibliographic record
Abstract
Some animals use humanmade objects for building and constructing nests or shelter and even for play. Gray wolves (Canis lupus) gather and use humanmade objects discovered in their natural environment. Gathering humanmade objects is a peculiar behavior particularly when there is no immediately apparent benefit to survival or reproduction. I opportunistically documented 46 different types of humanmade objects with plastic bottles and aluminum cans being the most common items found at wolf pup-rearing sites. Many objects were made of materials that appeared suitable to alleviate pain in teething pups. For some objects, however, it was not immediately obvious that they would alleviate teething pain due to their unpliable material. Additionally, such objects were quite rare in wolves’ natural environment although it was not uncommon to find them at pup-rearing sites. Rare humanmade objects may provide a novelty that stimulates pups more than common objects. I hypothesize that objects used by wolf pups 1) alleviate pain from teething, and 2) provide adults respite from energetic pups. The latter is an important distinction because it implies the benefit of object play is to the adults and not the pups per se. Gathering novel objects that occupy energetic and hungry pups may influence the overall ability of social carnivores to leave young unattended while they hunt, to rest upon their return, and ultimately rear young successfully.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".