Submerged aquatic vegetation and nitrogen retention data from 2012 to 2017 in lake Saint-Pierre, Saint Lawrence River
Bibliographic record
Abstract
Here we provide seven datasets that describes plant biomass (2012 to 2016), environmental variables and nitrogen retention time series (2012 to 2016) in a submerged aquatic vegetation (SAV) meadow at the confluence of two agricultural tributaries (Saint-François and Yamaska) with the St. Lawrence River in southern Lake Saint-Pierre. Version 2 adds the dataset 6 and 7. The seven datasets are: 1) Growing season (June 21 to September 22) daily environmental variables (water level, water temperature, light, tributaries input, and SAV biomass indicator) 2) Mean SAV biomass measured using rake or quadrat samples in the meadow 3) Modelled daily nitrate tributary inputs to the SAV bed 4) Daily nitrate output to the SAV bed estimated from a sensor 5) Daily nitrate budget 6) Hourly nitrate output to the SAV bed and signal decomposition from ensemble empirical mode decomposition (EEMD) 7) Hourly dissolved oxygen and gas exchange velocities at the SAV bed outflow for 2016 Original data comes from Lake Saint-Pierre, either from publicly available government agencies data, from a project led by the Groupe de recherche interuniversitaire en limnologie (GRIL, 2012-2015) and by Morgan Botrel Ph.D. candidate (2016-2017, Université de Montréal) or from Christiane Hudon (ECCC). Data were created for an article on climate-driven variation in nitrogen retention, led by Morgan Botrel and supervisor Roxane Maranger, with Christiane Hudon, James B. Heffernan and Pascale M. Biron (https://doi.org/10.1029/2022WR032678).
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".