08 Origins 44 Polar bears … one of a (created) kind
Bibliographic record
Abstract
Jim Martell wanted to hunt polar bears. So intense was his enthusiasm that he spent $50,000 for a permit, a guide and travel. The hunt took them out on the tundra in the northwest territory of Canada. The first bears they saw were far away in the distance, so they kept on searching. At last the opportunity came, although the polar bear in Jim’s sights looked a bit strange. It did not have white fur, but was more of a ‘dirty blond’. Jim got the prey he wanted. It was then that problems started. Jim was planning to take the skin back to his home in Idaho. But the question was raised whether the bear was actually a hybrid between a polar bear and a grizzly. News reached the Department of Environment and a wildlife officer came to confiscate the hide. If it was a grizzly, Martell could be in trouble as his permit was not for hunting grizzlies! He found himself facing a fine of $1,000 or a year in jail. DNA tests revealed that the bear was indeed a hybrid between a polar bear and a grizzly. 1,2 Local experts have expressed surprise because whilst hybrids have previously been reported in zoos, this is probably the first case ever seen in the wild. Normally, polar and grizzly bears are adversaries and this keeps them at a respectful separation. Furthermore, polar bears mate on ice and grizzlies mate on land. So, as a biological phenomenon, this hybrid is a real surprise. Foundations in theory Biologists have inherited a classification system originally developed by the Swede Carl Linnaeus (1707-1778). He devised a hierarchy of descriptive categories. Bears are chordates (phylum), they are mammals (class), and they are carnivores (order). These are the higherlevel categories in the classification scheme. Bears all belong to one family, the Ursidae. There are several genera, with each genus having one or more member species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.334 | 0.206 |
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".