Mercury, DOC and hydrology data of James Bay rivers from summer 2018 to summer 2019
Bibliographic record
Abstract
This dataset includes chemical and physical data measured in situ in surface water of natural and dammed rivers of Eastern James Bay, in Northern Quebec, Canada. Samples were collected by boat or helicopter, mostly hovering but sometimes landing on the water or on the shore, during sampling campaign of summer 2018, fall 2018, winter 2019, spring 2019 and summer 2019 (see data set for precise dates). Mercury samples were collected using a peristaltic pump, and an in-line Whatman 0.45 µm filtration capsule attached to the tubing in case of filtered samples, with a clean sampling protocol to avoid any contamination by trace metals. Dissolved organic carbon, colored dissolved organic carbon and water isotopes (deuterium excess) data are also included in this dataset. Land cover data was derived from Land use 2010 dataset (Agriculture and Agri-Foods Canada, 2015). Hydrological data used to calculate annual and seasonal discharges were obtained through Hydro Quebec from historical hydrometric stations of and from new hydrometric stations deployed in 2018 and 2019 for this project. The purpose of the data was to explore the biogeochemical drivers of mercury concentrations, exports and yields in large northern rivers of Eastern James Bay. All other relevant informations regarding this dataset are detailed in the MetaGRIL metadata platform under the name of this project : https://gril.flsh.usherbrooke.ca/metacatui/data
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".