Methods for interpreting an untested Darwinian conjecture
Bibliographic record
Abstract
Intraspecific variations are the differences in traits among individuals of the same species.These variations are what natural selection acts on, and thus are crucial to evolution by natural selection as proposed by Charles Darwin in his book On the Origin of Species, or the Preservation of Favoured Races in the Struggle for Life.In this text, Darwin proposed multiple sources of intraspecific variation although one of them has yet to be studied despite having been published more than 150 years ago.Darwin explained that traits which are extraordinary compared to the same trait in closely related species ought to be highly variable.In this thesis, I provide possible interpretations of this conjecture, and construct distinct methodologies for testing each interpretation.The quantity and quality of data collected did not allow a decisive test of the conjecture; however, the interpretations and methods described herein may be used to guide future work once the appropriate data are published.
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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 | 0.039 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.008 |
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".