Dissolved methane, carbon dioxide and limnological data from subarctic rivers, northern Québec, Canada
Bibliographic record
Abstract
Subarctic rivers of northern Québec (Nunavik, Canada) unveil a potential pathway for waterborne release of greenhouse gases generated through mobilization of organic matter in the large ambient reservoir of permafrost carbon (see e.g. Nordicana D48). In the warming Arctic, this contribution may substantially alter the current parameterization of global carbon balance and the corresponding climate feedbacks. This Nordicana D archive presents the concentrations of dissolved greenhouse gases (methane, carbon dioxide, and nitrous oxide) that were measured in water sampled in the Great Whale River (GWR), Sasapimakwananisikw (SAS) River, the Sheldrake River, and their major tributaries, in summer and winter (GWR). Additional data are presented for dissolved oxygen concentration, temperature, pH, salinity, turbidity, and conductivity. The concentrations of the dissolved greenhouse gases were determined by (1) gas chromatography of gas samples extracted by headspace equilibration in a 2L flask (details in Matveev et al 2019), and (2) direct measurement with methane and carbon dioxide profilers (METS) by Franatech GmbH (details in Matveev et al 2018). The water column values of dissolved oxygen concentration, temperature, pH, salinity, turbidity and conductivity were determined with either Hydrolab DS5, RBR Concerto, and/or YSI EXO2 profilers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".