A without-prejudice list of generic names of fungi for protection under the International Code of Nomenclature for algae, fungi, and plants
Bibliographic record
Abstract
As a first step towards the production of a List of Protected Generic Names for Fungi, a without-prejudice list is presented here as a basis for future discussion and the production of a List for formal adoption. We include 6995 generic names out of the 17072 validly published names proposed for fungi and invite comments from all interested mycologists by 31 March 2014. The selection of names for inclusion takes note of recent major publications on different groups of fungi, and further the decisions reached so far by international working groups concerned with particular families or genera. Changes will be sought in the Code to provide for this and lists at other ranks to be protected against any competing unlisted names, and to permit the inclusion of names of lichen-forming fungi. A revised draft will be made available for further discussion at the 10(th) International Mycological Congress in Bangkok in August 2014. A schedule is suggested for the steps needed to produce a list for adoption by the International Botanical Congress in August 2017. This initiative provides mycologists with an opportunity to place nomenclature at the generic level on a more secure and stable base.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.069 | 0.075 |
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".