<i>Fomitopsis betulina</i> . [Descriptions of Fungi and Bacteria].
Bibliographic record
Abstract
Abstract A description is provided for Fomitopsis betulina . Sporophores of this fungus are found on both living and dead trees, where the fungus causes a brown-rot decay of heartwood, eventually reducing the substratum to a red-brown friable mass. Some information on its associated organisms and substrata, dispersal and transmission, habitats and conservation status is given, along with details of its geographical distribution (Asia (China (Gansu, Heilongjiang, Jilin, Nei Mongol Autonomous Region, Shaanxi, Sichuan, Xinjiang Autonomous Region, Yunnan), India (Meghalaya, Uttarakhand), Iran, Japan, Kazakhstan (Akmola, East Kazakhstan, Kostanay, North Kazakhstan), Nepal, Russia (Altai Krai, Altai Republic, Amur Oblast, Chelyabinsk Oblast, Irkutsk Oblast, Kamchatka Krai, Khabarovsk Krai, Khanty-Mansi Autonomous Okrug, Krasnoyarsk Krai, Novosibirsk Oblast, Omsk Oblast, Primorsky Krai, Sakha Republic, Sakhalin Oblast, Sverdlovsk Oblast, Tomsk Oblast, Tyumen Oblast, Yamalo-Nenets Autonomous Okrug, Zabaykalsky Krai), South Korea, Uzbekistan), Central America (Belize, Panama), Europe (Åland Islands, Andorra, Austria, Belarus, Belgium, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Isle of Man, Italy, Latvia, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Romania, Russia (Bryansk Oblast, Chuvash Republic, Ivanovo Oblast, Kaliningrad Oblast, Kaluga Oblast, Kirov Oblast, Komi Republic, Kostroma Oblast, Krasnodar Krai, Kursk Oblast, Leningrad Oblast, Mari El Republic, Moscow Oblast, Murmansk Oblast, Nizhny Novgorod Oblast, Orenburg Oblast, Perm Krai, Pskov Oblast, Republic of Bashkortostan, Republic of Dagestan, Republic of Karelia, Republic of Mordovia, Republic of Tatarstan, Ryazan Oblast, Samara Oblast, Saratov Oblast, Smolensk Oblast, Tula Oblast, Tver Oblast, Udmurt Republic, Vladimir Oblast, Yaroslavl Oblast), Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, UK), North America (Canada (Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland and Labrador, Northwest Territories, Nova Scotia, Ontario, Prince Edward Island, Quebec, Yukon Territory), USA (Alabama, Alaska, Arkansas, California, Connecticut, District of Columbia, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Montana, Nebraska, New Hampshire, New Jersey, New York, North Carolina, North Dakota, Ohio, Oregon, Pennsylvania, Rhode Island, Tennessee, Texas, Vermont, Virginia, Washington, West Virginia, Wisconsin)), South America (Argentina, Colombia)).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".