MétaCan
Menu
Back to cohort

Effective Population Size: Biological Duality, Field & Molecular Approaches

2000· article· en· W4234865017 on OpenAlexfundno aff
Sarah A. Woodin, Michael Grove, Daniel D. Heath

Bibliographic record

VenueThe American Biology Teacher · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of South CarolinaNational Science Foundation
KeywordsIconCitationDownloadField (mathematics)PopulationComputer scienceWorld Wide WebLibrary scienceInformation retrievalSociologyMathematicsDemography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.633
Threshold uncertainty score0.856

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.072
GPT teacher head0.264
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueThe American Biology TeacherSame topicPlant and animal studiesFrench-language works237,207