<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)).
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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 | 0.003 |
| 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) | 0.035 | 0.029 |
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".