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Record W2768159037 · doi:10.46867/ijcp.2017.30.00.12

The Legacy Lives on, a Year Later: Dr. Stan A. Kuczaj A Special Issue –Part 2

2017· article· en· W2768159037 on OpenAlexaff
Holli C. Eskelinen, Heather M. Hill, Rachel T. Walker, Marie Trone

Bibliographic record

VenueInternational Journal of Comparative Psychology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsSt. Mary's University
Fundersnot available
KeywordsTributeHonorVisionTheme (computing)PublishingPublicationScope (computer science)SociologyLibrary scienceHistoryPsychologyArt historyPolitical scienceLawComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

The scientific community has mourned the loss of Dr. Stan Kuczaj, Professor at The University of Southern Mississippi and Director of the Marine Mammal Behavior and Cognition Laboratory, for the past year. In this time of grieving and reminiscing, his scientific legacy has continued to live on through students, collaborators and trusted colleagues. Stan’s passing has acted in part as a motivator to continue to publish works that he invested time and energy in as a tribute, seeing his visions through to fruition. In addition to publishing droves of literature, his colleagues within the development and comparative fields have bound together for the common goal of advancing the science through new collaborations, merged resources, and tackling innovative topics in comparative studies. This second commemorative special issue is a testament to the vast scope of Stan’s impact on the scientific community, as well as his legacy that each of his students and colleagues continues to cultivate. Ten additional papers round out our initial tribute to Dr. Stan Kuczaj in honor of his lifetime achievements.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.592
Threshold uncertainty score0.999

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.060
GPT teacher head0.400
Teacher spread0.339 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2017
Admission routes1
Has abstractyes

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