Bibliographic record
Abstract
INTRODUCTION It may seem improper to propose to speak of theories of heredity in seventeenth-century science. The transmission of traits through material units did not become a central topic of concern until the eighteenth century, in, for example, the work of Charles Bonnet and others impressed with Abraham Trembley's discovery in 1741 of the freshwater polyp's ability to regenerate itself from amputated bits – evidence that each bit of an organism carried in it some sort of plan for the organism as a whole. Indeed, in the early seventeenth century the idea of a program or a blueprint that specifies what a developing creature must become is precisely what the prevailing anti-Aristotelian spirit compelled researchers to reject in their accounts of animal development. “Heredity,” in the narrower sense that this term has today, refers only to those theories of the acquisition of traits by creatures that account for the mechanism by which information-bearing material causes a new creature to acquire the traits it does. But by “heredity” in a broader sense we may also understand any number of different kinds of transmission in sexual reproduction, all of which were clearly on the minds of early modern generation scientists. Generation theorists in the seventeenth century were, namely, intensely interested in determining the following: How it is that parents generate offspring with traits similar to their own. Any creature is more than just a perfect blend of equal parts of father and mother. It also bears resemblance to more distant relatives and frequently appears to have traits altogether dissimilar to those of any known ancestors. All copy is to some extent bad copy. Without knowledge of the mechanism of dominant and recessive genes, this fact presented a number of problems. […]
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.031 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".