Bibliographic record
Abstract
oremost, Williams College provided a stable environment for teaching and research over a thirty-five-year career that extended into retirement, with additional support through the Office of the Dean of Faculty.A generous subsidy toward publication of this book came from the Class of 1958 Fund for Faculty Enhancement at Williams College.Among a score of talented students, Hal Lescinsky, Ken MacLeod, and David Skinner accompanied the author to the shores of Hudson Bay at Churchill, Manitoba, to investigate Ordovician and Silurian paleoislands.On separate occasions, Dan Walsh and Jim Scott traveled to Western Australia to take part in studies on Devonian and Permian paleoislands, respectively.Marshall Hayes, Hovey Clark, and Jon Payne came to Mexico's Baja California peninsula to study the Cretaceous paleoislands around Eréndira on the Pacific Coast.Max Simian, Mike Eros, and Dan Perez made multiple trips to the Gulf of California to map Pliocene paleoislands, and Laura (Libby) Blackmore, Cordelia Ransom, Patrick Russell, and Peter Tierney did likewise with respect to Pleistocene paleoshores.In addition to their welcome company, these outstanding Williams students made independent contributions through their own vigilant field observations.Support for field studies in China's Inner Mongolia and in Western Australia came from the National Geographic Society through separate grants that enabled the material covered in chapters 4, 5, and 6 to be developed in full.Several grants from the Petroleum Research Fund through the American Chemical Society supported fieldwork on Cretaceous and Pliocene paleoislands covered in chapters 8 and 10.A grant from the Marion and Jasper Whiting Foundation enabled travel to South Korea's Hongdo (Red Island), described in chapter 1.Three years of grant support through the Spanish Ministry of Science and ACKNOWLEDGMENTS
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.437 | 0.121 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".