Fungal Community Ecology Using MALDI-TOF MS Demands Curated Mass Spectral Databases
Bibliographic record
Abstract
Authors thank professors Marc-André Lachance and GregThorn for critical reading on initial drafts of this piece. Mariana Cereia, English teacher, kindly proofread the final version. Authors also thank Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), Conselho de Desenvolvimento Científico e Tecnológico (CNPq) and National System for Research on Biodiversity (Sisbiota-Brazil, CNPq 563260/2010-6/FAPESP n◦ 2010/52322-3) for supporting previous work that led to this opinion piece. CS thanks the Universidad de La Frontera (Temuco, Chile) for the partial funding from Project DIUFRO DI18-0121. NL thanks the partial support by the Portuguese Foundation for Science and Technology (FCT), the strategic funding of the UID/BIO/04469/2019 unit, COMPETE 2020 (POCI-01-0145-FEDER-006684) and the BioTecNorte operation (NORTE-01-0145-FEDER-000004), funded by the European Regional Development Fund through Norte2020—Programa Operacional Regional do Norte.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".