Winter under-ice limnological and pigment data of the Great Whale River and its freshwater plume into Hudson Bay.
Bibliographic record
Abstract
Winter conditions profoundly alter the limnological properties of lotic ecosystems. In 2019, from February 25 to March 1, we sampled the under-ice water column of the Great Whale River and its plume into Hudson Bay. This limnological and pigment dataset is complementary to an amplicon dataset (prokaryotes and microbial eukaryotes) and a metagenomic dataset that are available at the NCBI Sequence Read Archive (BioProject PRJNA999265 and PRJNA999354). It includes data for nutrients (total nitrogen and phosphorus), carbon (dissolved organic and inorganic), dissolved organic carbon characterization (SUVA254, SR, a320, S289), cell abundance measured by flow cytometry, total suspended sediment concentration, specific conductivity, snow depth, ice thickness, under-ice water depth, oxygen concentration, temperature and pigments determined by high-performance liquid chromatography.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".