Bibliographic record
Abstract
Fungal genomicsFrom inconspicuous microorganisms to moulds to mushrooms, the biodiversity of filamentous fungi is enormous.They are the major decomposers of plant-derived biomass and play an essential role in biogeochemical cycling in the terrestrial biosphere.While edible mushrooms have been part of the human diet for over 2000 years, some filamentous fungi are the causative agents of animal and plant diseases.Filamentous fungi have been used as cell factories to produce antibiotics, therapeutics, enzymes and organic acids.The enzymes produced by them are used in food, feed, detergent, pulp and other industries.Since the publication of the genome of Phanerochaete chrysosporium [1], the first for filamentous fungi, over 400 filamentous fungal genomes have been sequenced or are being sequenced (http://gen omeonline.org).The genome-sequenced fungi fall into two broad groups, one with medical relevance [2] and the other comprising fungi with impact on biogeochemical cycling and biofuels development [3].Until now, over 75% of the genomes sequenced belong to four classes of the Fungal Kingdom: Agaricomycetes, Dithideomycetes, Eurotiomycetes and Sordariomycetes.To capture the biodiversity present in the Fungal Kingdom, the 1000 Fungal Genomes Project (www.1000.fungalgenome.org),initiated in 2011 in collaboration with the Joint Genome Institute of the U.S. Department of Energy, plans to sequence two species from every known family of the Fungal Kingdom.The completed and ongoing efforts in fungal genomes provide foundational information that will help researchers understand how fungi work in diverse ecologies.The articles in this special issue review features that are unique to filamentous fungi as unravelled by genomic approaches.The first article [4] addresses the approaches and resources available for post-genome sequencing research in filamentous fungi.Poorly assembled and characterized genomes make functional genomic analysis of many filamentous fungi challenging.For the hundreds of evolutionarily diverse genomes that
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.083 |
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".