Bibliographic record
Abstract
Abstract A description is provided for Calycina herbarum . Some information on its associated organisms and substrata, dispersal and transmission, economic impacts, habitats and conservation status is given, along with details of its geographical distribution (Africa (Morocco, Sao Tome and Principe), North America (Canada (British Columbia, Newfoundland and Labrador, Ontario, Quebec), USA (Arizona, California, Colorado, Idaho, Illinois, Indiana, Iowa, Maine, Massachussetts, Michigan, Minneapolis, Montana, New Hampshire, New Jersey, New York, North Carolina, North Dakota, Ohio, Oregon, Pennsylvania, South Carolina, Texas, Utah, Washington, West Virginia, Wyoming)), South America (Argentina, Brazil (Rio Grande do Sul), Chile, Colombia), Asia (Armenia, Azerbaijan, China (Anhui, Beijing, Qinghai), Republic of Georgia, India (Himachal Pradesh, Uttarakhand), Japan, Kazakhstan (Almaty Oblast, East Kazakhstan), Nepal, Philippines, Russia (Kamchatka Krai, Khabarovsk Krai, Khanty-Mansi Autonomous Okrug, Primorsky Krai, Sakhalin Oblast), South Korea, Turkey), Australasia (Australia (Victoria)), Europe (Andorra, Austria, Belarus, Belgium, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Ireland, Italy, Lithuania, Netherlands, Norway, Poland, Romania, Russia (Leningrad Oblast, Moscow Oblast, Smolensk Oblast), Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, UK)).
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".