STREAM Benthic Metabarcoding Lab Protocol v2
Bibliographic record
Abstract
STREAM (Sequencing the Rivers for Environmental Assessment and Monitoring; www.stream-dna.org) is a Canada-wide community-based science program established in 2018 which is led by the research laboratory of Dr. Mehrdad Hajibabaei at the University of Guelph' Centre for Biodiversity Genomics (Guelph, Canada), in collaboration with Environment and Climate Change Canada, and Living Lakes Canada. Using standardized training, field, laboratory, and bioinformatic protocols, communities can send freshwater benthic kick-net samples for DNA metabarcoding analysis, which allows for the rapid identification of benthic and diatom taxa within the sample. Through the duration of this program, over 2000 samples have been contributed and analysed to date. This protocol details the DNA metabarcoding lab methods developed for use by the Hajibabaei lab on STREAM. Also linked within the protocol are the STREAM Field Sampling Protocol, and details of bioinformatic processing through the MetaWorks (Porter and Hajibabaei, 2022) pipeline.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.091 | 0.111 |
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".