<i>Laetiporus sulphureus</i> . [Descriptions of Fungi and Bacteria].
Bibliographic record
Abstract
Abstract A description is provided for Laetiporus sulphureus growing on a wide range of woody plants. Some information on its taxonomy, morphology, dispersal and transmission and conservation status is given, along with details of its geographical distribution (Cameroon, Democratic Republic of the Congo, Equatorial Guinea, Ethiopia, Kenya, Senegal, South Africa, Tanzania, Tunisia, Armenia, Azerbaijan, China (Guangxi, Jiangxi, Sichuan, Xinjiang Autonomous Region), Republic of Georgia, India (Assam, Chhattisgarh, Himachal Pradesh, Jammu and Kashmir, Kerala, Meghalaya, Rajasthan, Sikkim, Uttarakhand, Uttar Pradesh, West Bengal), Indonesia, Iran, Israel, Japan, Kazakhstan (Aktobe, Almaty, East Kazakhstan, South Kazakhstan, West Kazakhstan), Laos, Mongolia, Nepal, Pakistan, Philippines, Russia (Altai Krai, Altai Republic, Amur Oblast, Irkutsk Oblast, Khabarovsk Krai, Khanty-Mansi Autonomous Okrug, Novosibirsk Oblast, Omsk Oblast, Primorsky Krai, Republic of Buryatia, Republic of Khakassia, Sakha Republic, Sakhalin Oblast, Sverdlovsk Oblast, Tomsk Oblast, Tyumen Oblast, Yamalo-Nenets Autonomous Okrug), Korea Republic, Taiwan, Turkey, Uzbekistan, Bermuda, Spain (Canary Islands), Australia (New South Wales, Northern Territory, Queensland, Tasmania, Victoria, Western Australia), New Zealand, Dominica, Guadeloupe, Jamaica, Trinidad and Tobago, Belize, Costa Rica, Honduras, Panama, Austria, Belarus, Belgium, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Irish Republic, Isle of Man, Italy, Latvia, Liechtenstein, Lithuania, Luxembourg, Montenegro, Netherlands, Norway, Poland, Portugal, Romania, Russia (Astrakhan Oblast, Belgorod Oblast, Bryansk Oblast, Chuvash Republic, Ivanovo Oblast, Kaliningrad Oblast, Kaluga Oblast, Kirov Oblast, Krasnodar Krai, Kursk Oblast, Leningrad Oblast, Moscow Oblast, Nizhny Novgorod Oblast, Novgorod Oblast, Orenburg Oblast, Penza Oblast, Pskov Oblast, Republic of Adygea, Republic of Bashkortostan, Republic of Dagestan, Republic of Mordovia, Republic of North Ossetia-Alania, Republic of Tatarstan, Samara Oblast, Saratov Oblast, Stavropol Krai, Tambov Oblast, Tula Oblast, Tver Oblast, Ulyanovsk Oblast, Vladimir Oblast, Volgograd Oblast, Voronezh Oblast, Yaroslavl Oblast), Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Ukraine, UK, Mauritius, Réunion, Canada (British Columbia, Manitoba, New Brunswick, Nova Scotia, Ontario, Quebec), Mexico, USA (Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, District of Columbia, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Hampshire, New Jersey, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin, Hawaii), Argentina, Brazil (Amazonas, Bahia, Distrito Federal, Espírito Santo, Paraná, Pernambuco, Rio de Janeiro, Rio Grande do Sul, Santa Catarina, São Paulo), Chile, Colombia, Ecuador, Guyana, Venezuela) and hosts ( Quercus, Salix, Prunus, Fagus and Populus spp.).
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.014 | 0.010 |
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".