Crustacean zooplankton and macroinvertebrate traditional taxonomy abundance data and whole community COI eDNA metabarcoding data from 13 high elevation Rocky Mountain lakes [Canada, 2018]
Bibliographic record
Abstract
Crustacean zooplankton, macroinvertebrates and eDNA (water filtration) samples were collected from 13 high elevation mountain lakes along a range of elevation and exotic brook trout abundance in the fall of 2018 in the Rocky Mountains, Canada. Traditional enumeration methods were performed on the zooplankton samples to the species level, and macroinvertebrate samples to the family level to obtain community abundance data. eDNA samples were sequenced using cytochrome c oxidase subunit I (COI) mitochondrial gene region. Sequences were assigned to ASVs. ASVs were analysed using sequences aligned to zooplankton, macroinvertebrates and whole community separately. In addition, 15 environmental variables were used to capture abiotic variation between lakes. The data files include the traditional abundance data, the ASV sequences, taxonomic assignments of sequences and site environmental data. We used this data to validate the known community patterns of zooplankton and macroinvertebrate communities known to occur in this system and then examine if eDNA metabarcoding using COI could detect these same patterns. We also evaluated long term impact of brook trout abundance on those communities along an elevation gradient.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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".