Knowledge gaps explored for Laboulbeniomycetes, a clade of understudied microfungi, including a new shortfall related to a lack of taxonomic expertise
Bibliographic record
Abstract
Despite escalating efforts to document and understand biodiversity, gaps in knowledge persist due to the inherent complexity of biological systems and the sheer scale of global biodiversity. Such knowledge shortfalls have been identified, described, and discussed for several well-studied organismal groups, leading to significant advancements in research and conservation. However, less-studied organismal groups (including most fungi) continue to suffer from pronounced knowledge gaps. Recently, Laboulbeniomycetes (phylum Ascomycota), a class of minute fungi obligately associated with arthropods either for dispersal or as epibionts, has been proposed as a model system to illustrate the extent of biodiversity and conservation shortfalls in poorly known organismal groups. Here, we explore biodiversity knowledge shortfalls for the Laboulbeniomycetes, examining gaps in species diversity (Linnean), distribution (Wallacean), populations (Prestonian), evolution (Darwinian), functional traits and ecological functions (Raunkiaeran), abiotic tolerance (Hutchinsonian), ecological interactions (Eltonian), natural history knowledge (Parkerian), persistence (Latimerian), conservation assessment (Scottian), and applied conservation, legal policies, fundings, and public awareness (Ostromian). Additionally, we introduce the Thaxterian shortfall, named after Roland Thaxter (1858–1932), professor at Harvard University and leading expert of Laboulbeniales, that quantifies the lack of taxonomic expertise in a given organismal group. We also discuss the implications of these shortfalls for conservation and future research avenues. We hope this chapter will serve as a catalyst for further discussion on biodiversity shortfalls in other taxa and pave the way towards a global momentum for the integration of poorly known organisms into conservation frameworks.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".