Bibliographic record
Abstract
A taxonomically diverse suite of fungi interacts with bryophytes as pathogens, parasites, saprobes, and commensals. Necrotrophic pathogens such as Tephrocybe palustris (Peck) Donk and Nectria mnii Döbbeler form patches of moribund gametophytes in otherwise healthy mats of mosses. These pathogens exhibit different methods of host cell disruption; N. mnii appears to displace the host cell protoplast with intracellular hyphae, while T. palustris causes host protoplast degeneration. Host responses to infection by bryopathogens are also variable. Host–pathogen relationships can be highly evolved, as in Bryophytomyces sphagni (Navashin) Cif., in which fungal propagules replace the bryophyte spores, and exploit the explosive dispersal mechanisms of the Sphagnum host. Bryophilous parasites tend to exhibit high tissue or cellular specificity with varying host specificity. For example, Octospora similis (Kirchstein) Benkert infects the rhizoids of species of Bryum, and Discinella schimperi (Navashin) Redhead specifically colonizes the mucilage producing cells of stems of Sphagnum squarrosum Crome. Eocronartium muscicola (Pers.) Fitzp. demonstrates a highly evolved host–parasite relationship in which the basidiocarp displaces the sporophyte and is fed directly by the gametophyte through specialized transfer tissues. Fungi such as Oidiodendron maius Barron are capable of decomposing moss cell walls that are generally resistant to decomposition because of their polyphenolic component. Mycorrhizal fungi, including Glomus, Suillus, and Endogone, have not been observed to form functional, nutrient-exchanging mycorrhizal interfaces with bryophytes, rather, they function as saprobes on moribund and senescent gametophytes. Finally, endophytic fungi may provide bryophyte hosts with greater tolerance to extreme pH or promote vegetative growth. In vivo observation of bryophyte–fungus interactions has provided insight into the types of interactions that occur; however to further understand the physiology, anatomy, and etiology of these interactions, it is necessary to culture bryophilous fungi in vitro and create artificial axenic systems for study.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".