Bibliographic record
Abstract
For reproductive behaviour and behaviour in zoos, see Chapters 9 and 10. How social animals behave among themselves is of great interest to behaviourists, but also to those who oversee wildlife populations (Carter, 2009). For example, if a contagious disease is present in one individual, how that animal interacts with others will help a manager figure out how the disease might spread, and therefore how it might best be combatted. Or if a few individuals are going to be transported to another area, picking giraffe who are friends to travel together will ease this process. Now that we know from DNA samples that most subspecies of giraffe have been isolated from each other for very long periods of time, and therefore may actually be separate species, it would be exciting to find whether these ‘species’ have unique social behavioural patterns too. This may be true, but we won’t know this any time soon. There are far too few studies of behaviour, each of which is incredibly labour-intensive and expensive. As well, it has already been shown, as we shall see in this chapter, that behaviour is shaped in part by local environments and by group composition. In this chapter it is assumed that giraffe of all races behave in more or less the same way.
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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 | 0.001 | 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) | 0.021 | 0.004 |
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".