Bibliographic record
Abstract
This special issue is being published to mark the passage of a century since Barnum Brown made a remarkable discovery in the badlands of Alberta. Brown was from the American Museum of Natural History (AMNH) in New York, and he had come to Alberta the previous year (1909) to check a report of dinosaur bones near the present-day town of Drumheller. He had been impressed enough to mount an expedition that set off in a flat-bottomed scow down the Red Deer River on August 3, 1910. As they floated downstream, they frequently stopped and looked for fossils in the badlands. On August 11th, they found what they thought was much of an Albertosaurus skeleton in hard rock. They realized by the next day, however, that they were in fact excavating the well-preserved bones of several individuals. In his fieldnotes, Brown’s assistant Peter Kaisen noted that the “whole top of a hill is nothing but a mass of bone. There are four hind feet in sight, vertebrae, and a lot of limb bones.” On September 4th, they loaded eight boxes of bones (the product of 29 man-days spent in the bonebed) on the scow and floated the expedition farther downstream. The specimens were shipped to New York, where they were prepared, put on display for a short time, and then mostly forgotten. Although Brown occasionally mentioned the 1910 discovery in papers (Brown 1914; Matthew and Brown 1923) and …
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.271 | 0.176 |
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".