Giving the Eastern Canadian Diatom Index (IDEC) a makeover using metabarcoding: first considerations and challenges
Bibliographic record
Abstract
The Eastern Canada Diatom Index (Indice Diatomées de l'Est du Canada; IDEC) was developed to assess stream biological integrity from diatom assemblages. Since the first version of the IDEC in the early 2000s, the number of sites monitored with this bioassessment tool has increased steadily in the province of Quebec. This resulted in a significant increase in samples, which in turn resulted in an increased need for highly qualified personnel to carry out microscopic diatom identification. Time constraints and costs also represent limitations of microscopic analysis. There is thus a necessity to develop a more rapid tool for diatom assemblage analysis to maintain high-quality diatom-based stream monitoring in the province of Quebec. DNA metabarcoding targeting diatoms represents an interesting avenue in overcoming the limitations associated with traditional taxonomic analyses by microscopy. This preliminary study was conducted to investigate the potential use of diatom DNA metabarcoding for assessing stream biological integrity in Quebec. A total of 56 sampling sites were visited for biofilm collection in August of 2019 and again in August of 2020 (except for 15 sites that were visited only one of the two years). Genomic DNA was extracted from freeze-dried biofilms and sequenced using HTS targeting the rbcL barcode. Overall, diatom assemblage structure using DNA metabarcoding was similar to that based on microscopy. IDEC scores calculated from microscopy- and DNA-derived diatom assemblages were also comparable. The results are promising and justify conducting a similar study at a larger scale with more diverse stream types, regions, and impairment status, with the final objective of developing a new version of the IDEC based on diatom DNA.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".