Taxonomic or trait assignation of environmental sequences in regions with a poor taxonomic knowledge: case of river diatom metabarcoding with a new version of the annotated reference library Diat.barcode - supplementary data
Bibliographic record
Abstract
Supplementary data of the manuscript --- 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 and curated by a college of experts. 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. We show that a direct bioinformatic assignment of environmental sequences to traits has a strong interest 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%). Using co-correspondence analyses, we show that species assignation dataset and trait assignation datasets were significantly correlated in 7 out of 13 cases in French Guyana, whereas they were always significantly correlated in Mainland France. This can be interpreted as an important loss of ecological information with species assignation in French Guyana, which is not observed in mainland France. This shows the value for ecological studies to use direct assignation of environmental sequences to traits in regions where taxonomic knowledge is poor.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".