"The Macrolichens of New England" by James W. Hinds and Patricia L. Hinds. 2007. [book review]
Bibliographic record
Abstract
to those who follow grass taxonomy, including the segregations and realignments of species within genera in the tribe Stipeae.The content of the book generally is of high quality, and, as much as is possible with a group of plants that has its own set of descriptive terminology, the text and keys are readable with jargon minimized.There are occasional inconsistencies in the text; for example, in the treatment of Digitaria sanguinalis, under Origin, the species is said to be native, but in the Comments section, it is stated to be "a European species now established as a global weed."I detected relatively few proof-reading errors (e.g., synonym not italicized, punctuation misplaced, rare spelling errors), and these do not detract substantively from the book.This book provides a welcome updated treatment of the grasses of Colorado.In spite of the fact that there is a recent North American taxonomic treatment of the family, there will always be a need for regional and local treatments, especially for large and diverse families such as this.The book should prove to be useful for the identification of grasses in several adjacent states, including much of the upper Midwest, from Montana to North Dakota and south to Kansas.It should also be useful in the southern portions of the Prairie Provinces.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".