Bibliographic record
Abstract
Giraffe are good for zoos because they attract people intrigued by this amazing species. They are bad for giraffe, though, who are used to wide open spaces and a huge quantity of various leaves on which to munch. Fortunately, zoos have basic standards for animal welfare: animals must have freedom from disease, the possibility of reproducing and a long life (Bashaw et al ., 2001). Recently, the importance of psychological welfare has also been advocated which involves the reduction of negative stress, boredom and trauma for animals. Animals in the wild encounter new experiences every day; if possible, they should have similar stimulation in captivity. These standards apply to all species, including giraffe. To some extent, the health of a giraffe can be estimated from what it looks like. Members of Disney’s Animal Kingdom asked North American facilities holding giraffe to send them digital photos of their animals so they could compare and score them as to body type and conditioning (Christman, 2008). Seventy institutions responded. Seven knowledgeable experts then rated the 100 most clear images on a scale of one to five, one being giraffe in the poorest (emaciated) condition and five the fattest/obese animals. This comparison made it possible for keepers to decide if their own giraffe needed a change in diet or other conditions, which they were urged to undertake cautiously and under the care of a veterinarian. Most obviously, having a thin neck and bony prominences indicate a giraffe in poor condition.
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 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.058 | 0.015 |
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".