Bibliographic record
Abstract
Abstract Gregor Mendel (1822–1884) was a Moravian biologist from whose quantitative plant breeding studies were derived laws of inheritance that founded a new branch of biology, genetics. He crossed varieties of peas and examined the distribution of characters among offspring over successive generations. Hybrids resulting from crossing two varieties with contrasting characters did not breed true. They were not the ‘constant hybrids’ much sought by professional breeders that might lead to independent species. In the 1880s Mendel's work was appreciated by Wilhelm Focke and George Romanes, who initiated similar studies with animals. Studies with plants were initiated in the 1890s by European botanists who discovered Mendel's work in 1900. A relationship of hereditary units that determined characters – now known as genes – to chromosomes was noted by cytologist Michael Guyer in 1900. The major early advocate of Mendel's work, zoologist William Bateson, agreed with Mendel that new species could emerge discontinuously. Key Concepts For his experiments it was essential that Mendel chose characters in pea plants that would breed true. He found that hybrids between lines which themselves bred true, did not breed true. They were not ‘constant hybrids’. At that time many thought that constant hybrids would indicate a continuous species origination process. From ‘brother‐sister’ matings of pea hybrids, Mendel derived quantitative laws. His units that determined characters – now known as genes – were located to chromosomes by Michael Guyer. Without knowledge of Mendel's laws, European botanists rediscovered them in plants. William Bateson confirmed that Mendel's laws applied to various animal and plant species. Mendel's laws were challenged by mathematical biologists (biometricians). Mendel followed the statistics of his time and his results have withstood the test of time. Mendel's view that new species can arise discontinuously without the involvement of natural selection has gained support.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.288 | 0.144 |
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".