Bibliographic record
Abstract
Abstract A description is provided for Gyromitra esculenta , usually found on conifers, mostly on pines ( Pinus spp.). Some information on its morphology, habitat, dispersal and transmission and conservation status is given, along with details of its geographical distribution (Algeria, Morocco, Canada (Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Nova Scotia, Ontario, Prince Edward Island, Quebec, Saskatchewan, Yukon Territory), Mexico, USA (Alaska, Arizona, Arkansas, California, Colorado, Idaho, Illinois, Indiana, Kansas, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Montana, New Hampshire, New Mexico, New York, Ohio, Oregon, Pennsylvania, Tennessee, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin, Wyoming), Costa Rica, Argentina, Chile, China (Heilongjiang), Republic of Georgia, India (Jammu and Kashmir), Japan, Kazakhstan (Akmola Oblast, Almaty Oblast, East Kazakhstan Oblast, Karaganda Oblast), Pakistan, Russia (Irkutsk Oblast, Khanti-Mansi Autonomous Okrug, Kamchatka Oblast, Khabarovsky Krai, Krasnoyarsky Krai, Primorsky Krai, Tyumen Oblast), Turkey, Spain (Canary Islands), Australia (New South Wales), New Zealand, United States Virgin Islands, Dominican Republic, Andorra, Austria, Belarus, Belgium, Bulgaria, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Italy, Latvia, Lithuania, Luxembourg, Republic of Macedonia, Montenegro, Netherlands, Norway, Poland, Portugal, Romania, Russia (Kirov Oblast, Leningrad Oblast, Smolensk Oblast, Voronezh Oblast), Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine and UK) and hosts (conifers, mostly Pinus spp.).
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".