Bibliographic record
Abstract
The idea for this book emerged in the late 1980s when I attempted to find an interesting Darwin quote to add to a volume I was then editing on the impact of El Niño on Peruvian fishes and fisheries. I remembered from earlier readings that Darwin had been in Peru, where he collected specimens of the species which Leonard Jenyns later described as Engraulis ringens , the Peruvian anchoveta. But I did not find any suitable quote: the indexes of books by, or about, Darwin that I consulted all covered ‘finches’ but not ‘fishes’. Still, the pun was obvious, and I decided to write a short essay on Darwin's work on fishes, to be titled Darwin's Fishes , if only to get it out of my system. However, caught in the iron grip of the Law of Unintended Consequences, I ended up writing a book-size chrestomathy. Fortunately, I had the help of Darwin (who contributed about 45 000 of his words, i.e. almost one third of the entire book) and, as we shall see, the help of friends who provided relevant information and helped verify facts. The book now completed, I will attempt to cover my tracks, and pretend that this was written to fill the ‘major gap in scholarship’ that is usually recruited in such cases.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.321 | 0.178 |
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".