Paradise is Dynamic: Florida's Changing Landscape
Bibliographic record
Abstract
The following interview was conducted in the summer of 2020. Thomas Chesnes, the interviewee, is long-time professor of biology at Palm Beach Atlantic University in West Palm Beach, Florida. A native Floridian, Chesnes completed all of his degrees at the University of Florida in Gainesville (Ph.D., environmental engineering sciences/systems ecology/wetlands; M.S., environmental engineering sciences/estuarine systems; B.S., zoology). His research has focused on seagrasses, soil salinity, the Gulf killifish, the mangrove saltmarsh snake, the Atlantic red snapper, and all matters pertaining to wetlands, coastal habitats, and aquatic environments. For over a quarter of century, with numerous grants awarded (including a $50,000 grant from the Community Foundation for Palm Beach and Martin Counties), he has done fieldwork throughout the state, from North Florida (the St. Johns River area) to Florida Bay (which includes the Keys and the Everglades). He has also directed numerous trips to the Galapagos Islands. Until his recent appointment as associate dean of the School of Arts & Sciences, he was chair of his biology department. Perhaps most importantly for this interview, Chesnes draws from his scientific research some deeply reflective and philosophical insights.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".