<i>Fuligo septica</i> . [Descriptions of Fungi and Bacteria].
Bibliographic record
Abstract
Abstract A description is provided for Fuligo septica , a myxomycete which occurs on litter, fallen leaves, bark, decorticated branches, rotten stumps, fallen trunks, rotten wood and burnt logs of a very wide range of plants. Some information on its associated organisms and substrata, interactions and habitats, economic impacts, intraspecific variation, dispersal and transmission and conservation status is given, along with details of its geographical distribution (AFRICA: Algeria, Burundi, Democratic Republic of the Congo, Equatorial Guinea, Eritrea, Lesotho, Liberia, Madagascar, Malawi, Mayotte, Morocco, Nigeria, Sierra Leone, South Africa, Tanzania, Tunisia, Uganda, Zimbabwe; NORTH AMERICA: Canada (Alberta, British Columbia, Manitoba, New Brunswick, Newfoundland, Northwest Territories, Nova Scotia, Nunavut, Ontario, Prince Edward Island, Quebec), Mexico, USA (Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, Nevada, New Hampshire, New Jersey, New Mexico, 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, Wyoming), Mexico; CENTRAL AMERICA: Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, Panama; SOUTH AMERICA: Argentina, Bolivia, Brazil (Bahia, Maranhão, Paraiba, Pernambuco, Roraima, Santa Catarina, São Paulo, Sergipe), Chile, Ecuador (including Galapagos), French Guiana, Guyana, Paraguay, Peru, Uruguay, Venezuela; ASIA: Brunei, China (Fujian, Guizhou, Jiangsu, Zhejiang), Georgia, India (Assam, Chandigarh, Himachal Pradesh, Tamil Nadu, Uttar Pradesh, Uttarakhand), Indonesia, Iran, Japan, Jordan, Kazakhstan (Akmola, Aktobe, Almaty, East Kazakhstan, Karaganda, former Kokshetau, Kostanai, North Kazakhstan, Pavlodar, former Tselinograd, West Kazakhstan), Malaysia, Nepal, North Korea, Pakistan, Papua-New Guinea, Philippines, Russia (Altai Krai, Khanty-Mansi Autonomous Okrug, Krasnoyarsk Krai, Magadan Oblast, Novosibirsk Oblast, Tyumen Oblast), Singapore, South Korea, Turkey, Uzbekistan, Vietnam; ATLANTIC OCEAN: Spain (Canary Islands); AUSTRALASIA: Australia (New South Wales, Queensland, South Australia, Tasmania, Victoria, Western Australia), New Zealand; CARIBBEAN: American Virgin Islands, Antigua and Barbuda, Cuba, Dominica, Dominican Republic, Guadeloupe, Jamaica, Martinique, Puerto Rico, Saint Lucia, Trinidad and Tobago; EUROPE: Andorra, Austria, Belarus, Belgium, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, Moldova, Netherlands, Norway, Poland, Portugal, Romania, Russia (Astrakhan Oblast, Chelyabinsk Oblast, Chuvash Republic, Kaliningrad Oblast, Komi Republic, Krasnodarsk Krai, Kursk Oblast, Leningrad Oblast, Moscow Oblast, Murmansk Oblast, Orenburg Oblast, Pskov Oblast, Republic of Karelia, Stavropol Krai, Tver Oblast, Volgograd Oblast), Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, Ukraine, UK; INDIAN OCEAN: Christmas Island, Mauritius, Réunion, Seychelles; PACIFIC OCEAN: French Polynesia, Marshall Islands, New Caledonia, Solomon Islands, USA (Hawaii)).
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) | 0.001 | 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".