Data on carbon, nitrogen, and phosphorus forms in a north temperate river, Rivière du Nord, Québec, Canada, from 2017 to 2019
Bibliographic record
Abstract
The Rivière du Nord was sampled at 13 sites along its mainstem once per season from summer 2017 to winter 2020. Sites are numbered by river kilometer (RKm) with the outlet being RKm 0. The dataset includes concentrations (ug/L or mg/L as noted) of total organic carbon, dissolved organic carbon, particulate organic carbon, total nitrogen, total dissolved nitrogen, nitrate, ammonium, dissolved organic nitrogen, total phosphorus, total dissolved phosphorus and particulate phosphorus. It also includes fluorescence metrics derived from PARAFAC EEMs: fluorescence intensities (in Raman units) of 5 dissolved organic matter components (C1-C5), and 5 indices (SUVA-254, CDOM, FI, b:a, HIX). We sampled an additional 12 sites to act as endmembers (5 mainly forested sites, 5 mainly agricultural sites, and 2 wastewater treatment plant measures). Data were used to calculate C:N:P stoichiometry in the paper "Different forms of carbon, nitrogen, and phosphorus influence ecosystem stoichiometry in a north temperate river across seasons and land uses" (https://doi.org/10.1002/lno.11960). Data were used to quantify changes in organic matter composition in the paper "Contrasting seasons and land uses alter riverine dissolved organic matter composition" (https://doi.org/10.1007/s10533-022-00979-9).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| 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) | 0.007 | 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 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".