The Legacy Lives on, a Year Later: Dr. Stan A. Kuczaj A Special Issue –Part 2
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.022 |
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".