Environmental DNA (eDNA) data from the 2019 and 2020 Gulf of Alaska International Year of the Salmon Expeditions
Bibliographic record
Abstract
This dataset contains environmental DNA (eDNA) data collected in the Northeast Pacific Ocean. These data were collected as part of the International Year of the Salmon (IYS) Gulf of Alaska High Seas Expedition conducted in March and April 2020 and 2021, to further improve the understanding of factors impacting salmon early marine winter survival. eDNA analysis uses the free DNA shed from organisms and available in the environment to assess species diversity and composition. The eDNA database will provide overall composition of nekton, micronekton and zooplankton allowing estimation of salmon prey and potential predators. Water collected with Niskin bottle from 2-3m. 2L filtered onto Sterivex column for each replicate. Filters flash frozen. DNA extracted from filters using DNeasy kits (QUIAGEN). 16S and COI rRNA gens were amplified with PCR and sequenced on Illumina MiSeq platform using SE at 300 cycles. Reads were processed using obitools (https://pythonhosted.org/OBITools/welcome.html) and queired against nt using BLASTn. Reads were assigned to OTU using MEGAN (https://uni-tuebingen.de/fakultaeten/mathematisch-naturwissenschaftliche-fakultaet/fachbereiche/informatik/lehrstuehle/algorithms-in-bioinformatics/software/megan6/). Results were filtered using a cuttoff of >/=10 reads for positive detection. Detection of obvious contaminations belonging to human or food waste (sheep, pig, chicken, cow) as well as artificial positive controls were removed. For detailed information contact Dr. Christoph Deeg: chdeeg@mail.ubc.ca
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".