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Record W3026683141 · doi:10.1525/abt.2012.74.1.3

Thank You, <i>ABT</i> Reviewers

2011· article· en· W3026683141 on OpenAlexaboutno aff
William R. Leonard, Kathleen Westrich

Bibliographic record

VenueThe American Biology Teacher · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiverse Educational Innovations Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCitationIconLibrary scienceDownloadWorld Wide WebComputer science

Abstract

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Research Article| January 01 2012 Thank You, ABT Reviewers William Leonard, William Leonard Search for other works by this author on: This Site PubMed Google Scholar Kathleen Westrich Kathleen Westrich Search for other works by this author on: This Site PubMed Google Scholar The American Biology Teacher (2012) 74 (1): 7–8. https://doi.org/10.1525/abt.2012.74.1.3 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 William Leonard, Kathleen Westrich; Thank You, ABT Reviewers. The American Biology Teacher 1 January 2012; 74 (1): 7–8. doi: https://doi.org/10.1525/abt.2012.74.1.3 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentThe American Biology Teacher Search The National Association of Biology Teachers would like to thank the individuals listed below for their thoughtful reviews of manuscripts submitted to the American Biology Teacher during the year 2011. It is through their efforts that the ABT continues to be a valued and respected biology education journal. Chris Adams, King College, TN; Amelia Ahern-Rindell, University of Portland, OR; Christina Alevras, Saint Joseph College, CT; Douglas Allchin, University of Minnesota; Revathi Ananthakrishnan, Cambridge, MA; David Argent, California University of Pennsylvania; Roman Asshoff, University of Münster, Germany; Katharine Atkinson, Prince of Peace Schools, IA; Francisco Javier Aznar, University of Valencia, Spain; Gail A. Baker, Lane Community College, OR; Ana Barahona, UNAM, Mexico; Rob Barber, University of Wisconsin-Parkside; Laura M. Barden-Gabbei, Western Illinois University; Cindy Barlock, Kennedy High School, IA; Natalie Barratt, Baldwin-Wallace College, OH; Lauralee Barton, Riverside, CA; Roberta Batorsky, Collegeville, PA; Erin Baumgartner, Western Oregon University; Sandhya Baviskar, University of... 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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.118
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score0.322

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.118
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.003
Science and technology studies0.0050.002
Scholarly communication0.0120.005
Open science0.0030.004
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0960.125

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 head0.093
GPT teacher head0.276
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainEvaluation
GenreCommentary

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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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