Moss-associated epiphytic diatom (Bacillariophyta) occurrences in the Hudson Bay Lowlands: a proof of concept for non-destructive sampling of herbarium specimens
Bibliographic record
Abstract
Diatoms constitute important components of moss microbiomes. In the peatlands of the Hudson Bay Lowlands (HBL), where bryophytes (mosses and liverworts) cover most of the landscape, epiphytic diatoms are sensitive ecological indicators that contribute to primary productivity and biodiversity. However, they remain largely uninventoried and collecting from remote regions such as the HBL is resource-intensive. Sampling epiphytic diatoms traditionally uses destructive techniques on invaluable herbarium specimens. A simple, non-destructive technique for sampling and documenting epiphytic diatoms in herbarium specimens was tested and found to yield similar results in terms of species detection as common destructive acid digestion techniques. Diatoms were observed in 49/66 subsamples. Fifty-five genera were detected, including Eunotia, Nitzschia, Pinnularia, Navicula, Encyonema, Kobayasiella, and Gomphonema. These results demonstrate that herbarium specimens can provide a viable complement to field collection in biodiversity studies, in ways that not only preserve but also extend the value of the original specimen. This approach adds to the growing array of applications of physical herbarium specimens and extended herbarium data in support of current and future efforts to address challenges related to nature and natural resources.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".