Ferns, Spikemosses, Clubmosses, and Quillworts of Eastern North America
Bibliographic record
Abstract
I have had the pleasure of knowing Dr. Emily Sessa, the author of this extraordinary field guide, for over 15 years. Our paths first crossed at a field course on Tropical Ferns and Lycophytes in Costa Rica, sponsored by the Organization for Tropical Studies. Since then, we have regularly seen each other at botanical conferences, workshops, and American Fern Society meetings, among others. When I learned that Emily was embarking on the ambitious project of writing this book, I was both amazed and thrilled. Creating a comprehensive guide covering such an extensive region—from Peninsular Florida and the outer southern coastal plains to the central United States, the northern Midwest, the Northeast, and Canada—is a big task, and Emily has accomplished it with remarkable skill and dedication. In 2023, during the Botanical Society of America meetings in Boise, Idaho, Emily shared with us some of her field stories while working on the book. She even showed us some of the pages she was proofreading, and I was struck by the sheer amount of work and passion she had poured into this project. Now, holding the finished book in my hands, I can say that my excitement was well-founded.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".