Potential for microbially mediated nitrogen transformations in benthic algae, sediment, and overlying water in the Great Lakes, 2022
Bibliographic record
Abstract
This dataset is associated with an examination of environmental DNA (eDNA) obtained from freshwater matrices (i.e. benthic algae, sediment, and near bottom water) collected by scuba divers from previously established transects located along the U.S. shoreline of Lakes: Michigan, Huron, Erie, and Ontario. 16S rRNA gene amplicon sequencing (i.e., targeting bacterial communities) and high-throughput quantitative PCR targeting various N-cycle associated genes [the Nitrogen Cycle Evaluation (NiCE) chip] were performed to assess potential abundance and diversity of microbes involved in nitrogen transformations including nitrogen fixation. Sample-associated sequences are available in NCBI Bioproject: PRJNA1253336. All eDNA samples for this dataset were collected alongside a larger body of work conducted in 2022 (https://doi.org/10.5066/P13JDUMH) and relate to multiple years of work at these stations: briefly, algal and dreissenid mussel biomass, water quality assessments, and diver observations of dreissenid mussels, round gobies, benthic substrate, and benthic algal cover. We refer to the benthic algae also as the ‘Cladophora community’ and ‘submerged aquatic vegetation (SAV)’ in other published project data, which were collected starting in 2018 (Great Lakes Science Center, 2018).
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".