Mercury concentrations and water chemical variables measured in La Romaine hydroelectric reservoir complex in the summer of 2016, 2017 and 2018.
Bibliographic record
Abstract
This dataset includes chemical and physical data measured in situ in water in natural and recently dammed portions of La Romaine River watershed in Northern Quebec, Canada. Samples were collected from 2016 to 2018 in August, as well as in June 2017. For most sites, the samples were collected close to the water surface (30 cm deep) with a peristaltic pump following the clean hands dirty hands sampling protocol to avoid any contamination by trace metals. Filtered samples were collected using an in-line Whatman 0.45 µm filtration capsule attached to the tubing. For June 2017, physicochemical variables collected with a YSI multiprobe, greenhouse gas partial pressure (methane and carbon dioxide), total nutrient concentrations and dissolved metals (iron and manganese) are also included. We used this dataset to explore the distribution in time and space of various forms of mercury and pools of carbon across the dammed watershed of La Romaine and in different aquatic systems (e.g. tributaries, lakes, groundwater, river and reservoir sites).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".