Bibliographic record
Abstract
Wild orchids bloom in virtually every habitat of every state and province of the continental United States, Canada, and Greenland. Orchid fanciers and collectors - a large and fervent segment of the general public - will welcome Paul Martin Brown's comprehensive, illustrated checklist and field guide to the exotic world of these elegant and intriguing flowers. This annotated guide is packed with up-to-date information and enhanced by stunning color photographs and extraordinary drawings of each species, subspecies, and variety, many highlighting unusual color or growth forms. It provides identification, full distribution range, recent synonyms, and all subspecies varietal and forma information for all 247 taxa as well as comments about the special aspects of each species. Taxonomy and distribution data directly complement information in the Flora of North America project and the parallel dichotomous keys will be useful in the field. The guide covers 223 species, 24 subspecies and varieties, 103 growth and color forms, and 24 hybrids. With its personal checklist and easy-to-read format, Wild Orchids of North America is perfect for the hobbyist, while offering a concise scientific reference for naturalists, botanists, and advanced orchid enthusiasts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".