MIGRATION OF THE NORTH AMERICAN MONARCH DANAUS PLEXIPPUS TO CUBA By
Bibliographic record
Abstract
by Cristina DockxTo Nature’s beauty and magicACKNOWLEDGMENTS I would like to thank my advisor Lincoln Brower for introducing me to the monarch butterfly and to the students from his laboratory (who became very good friends as did the monarch butterfly, “the monarch of the team”); and for letting me work in what at that time seemed a “crazy ” project; the migration of the monarch butterfly to Cuba. I would like to thank the members of my committee: Richard Kiltie, Thomas Walker, Jackie Miller and Jon Reiskind, all of whom contributed to the success of my research. I extend special thanks to Richard Kiltie, who has been a supportive, patient, and critical member. Thomas Walker provided me with constructive criticism. Jackie Miller was always interested in my work, and offered me her direct knowledge of monarchs in Cuba. Jon Reiskind always had a smile and a hand when I needed it. for being a part of this project. Sandy Lapis and Richard engaged in the big enterprise of smoothing my grammar and making my ideas flow. This is a tenacious endeavor since English is not my native language. I thank them. I thank my friends, Magola Molina (my mom), Tonya VanHook, Amy Knight and Tom Wunderli (my husband), who gave me logistic support: my husband with his wonderful dinners, Tonya for helping me survive the first years of graduate school, Amy for listening and helping me with the development of this project, and Magola for being “my assistant ” in the field and the laboratory. iv The Tinker Foundation and Sigma Xi provided financial support for the first year of field work. The National Science Foundation grant (Lincoln P. Brower, principal investigator) financed the chemical analyses of the butterflies. Leonard Wassenaar and Keith Hobson (Canadian scientists) finance the majority of the isotopic analyses, I thank them. Lincoln Brower (my advisor) and Richard Wunderli helped me with personal money to pay for the isotopic analyses. I am grateful to them.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".