Bibliographic record
Abstract
Hundreds of social species mingle freely with other social species if they are not predators (which, by contrast, except for lions are usually solitary rather than gregarious). Zebras forage alongside wildebeest, waterfowl and shore birds of many species mix together, colonial species of marine birds crowd on cliff faces, and a variety of birds gather at bird feeding stations. Such sociality may stress feeding resources, but it improves safety for the many animals. Here, however, we are interested in special friendships between two or a few adults of different species. Newspapers often run fetching pictures of two such adult animals cozying up together (and many more involving a cute infant, which do not concern us here). In my “Interspecies Friends” file I have best buddy photographs of a Siamese cat and a parrot, a red setter and a hamster, two swans and a goose, a deer and a horse, a poodle and a budgie, dogs and rabbits, a dog and a fawn, and a cat and a squirrel, all taken by or for the proud human companions of these odd couples. Often one of these animals was an orphaned youngster when it met its pal; if animals become familiar with each other from an early age, they are often fast friends for life. Early experiments have shown that when a kitten is raised with a rat, the cat will not kill its friend, even though it may kill other rats (Kuo, 1930). Animal Sanctuaries are where such curious friendships flourish, genial homes as they are to needy animals of various species (Hatkoff, 2009).
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.224 | 0.064 |
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".