Bibliographic record
Abstract
vi in my bibliography, Butch has been extremely influential in shaping my scientific worldview.Emília Martins has provided invaluable statistical advice and has stimulated me to think comparatively and historically.Dale Sengelaub has been the consistent voice of pragmatism on my committee, reminding me to always keep mechanism in mind.Although my name appears alone on the title page, I could not have done any of this work by myself.My dissertation work has been largely the result of being in the right place, surrounded by the right people, at the right time.Jodie Jawor not only did two years worth of hormone assays that form a major basis for Chapters 3-5, she also helped me in the field in two different summers, taught me how to do my own hormone assays, and collaborated with me on numerous other projects.Joe Casto (Chapter 3) was the first to figure out how to inject a junco with GnRH.Joe also worked with me in the field during my first full season at Mountain Lake, helping me to get the ball rolling on my fieldwork.Tim Greives worked with me in the field for two years, helped me develop Chapter 3, and has been great to talk science with in general.I thank Val Nolan for his work on Chapter 2. Val also helped me write my very first scientific paper.I have a vivid memory of receiving the manuscript after he had read it.It was covered in pencil marks which managed to transform my pedestrian prose into something quite elegant.I thank Patty Parker, at University of Missouri, St. Louis, for orchestrating the molecular work that was crucial to Chapter 2, and Jenny Phillips for her work as an REU student that was included in Chapter 3. Finally, I want to thank Danielle Whittaker
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".