Effective Population Size: Biological Duality, Field & Molecular Approaches
Bibliographic record
Abstract
Research Article| January 01 2000 Effective Population Size: Biological Duality, Field & Molecular Approaches Sarah A. Woodin, Sarah A. Woodin Search for other works by this author on: This Site PubMed Google Scholar Michael Grove, Michael Grove Search for other works by this author on: This Site PubMed Google Scholar Daniel D. Heath Daniel D. Heath Search for other works by this author on: This Site PubMed Google Scholar The American Biology Teacher (2000) 62 (1): 51–57. https://doi.org/10.2307/4450826 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Sarah A. Woodin, Michael Grove, Daniel D. Heath; Effective Population Size: Biological Duality, Field & Molecular Approaches. The American Biology Teacher 1 January 2000; 62 (1): 51–57. doi: https://doi.org/10.2307/4450826 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentThe American Biology Teacher Search This content is only available via PDF. Copyright The National Association of Biology Teachers Article PDF first page preview Close Modal You do not currently have access to this content.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".