Assigning taxonomy and traits to DNA sequences of river diatoms in a region with limited taxonomic knowledge using the updated and annotated reference library Diat.barcode
Bibliographic record
Abstract
We present a new version of a barcoding reference library dedicated to diatoms, Diat.barcode v12, with newly published sequences, annotated with ecological, and biological traits (size class, life forms, ecological guilds, etc ) and curated by a college of experts. Diat.barcode incorporates rbcL data on all diatoms, though freshwater taxa are better represented. We used this library in two different areas, one where the taxonomic coverage of the library was good (mainland France) and another where it was poor (French Guyana) with about 320 diatom samples collected for river monitoring across both regions. We show that a direct bioinformatic assignment of environmental sequences to traits (ecological guilds, habitats, morphology) has great potential in French Guyana where species knowledge is poor and therefore the proportion of assigned environmental sequences is much lower (12.8%) than trait assignation (30%). We used co-correspondence analyses to show that, unlike mainland France where all species and trait assignation datasets were significantly correlated, in French Guyana only 7 out of 13 trait categories showed a significant correlation. This indicates a significant loss of ecological information through species assignment in French Guyana. Consequently, directly assigning environmental sequences to traits can be useful and provide more ecological information in regions with poor taxonomic knowledge because of the many rbcL sequences without taxonomic assignations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".