Data on submerged aquatic vegetation and its water environment in Lake Saint-Pierre, Saint Lawrence River, from 2012 to 2016
Bibliographic record
Abstract
This dataset is the result of a large collaborative work lead by the GRIL from 2012 to 2015 on a submerged aquatic vegetation meadow located downstream of two agricultural tributaries (Saint-François and Yamaska rivers) in Lake Saint-Pierre, a fluvial lake of the Saint Lawrence River. The data describe plants (as rake biomass and echosounding) and their environment, including water chemistry, current velocity as well as light, temperature and instantaneous meteo. Only echosounding data are available in 2016 and sediments were collected in 2015. Data are organized as a relational database and the GRIL_LSP_database.png provides keys and links between tables as well as data format. Data are in the tables mesure_integree, mesure_spatiale, mesure_verticale, plante_biomass_taxon, plante_recolte, plante_in_situ. The other tables are metadata about spatiotemporal locations and reported measures. Additional data (e.g. zooplankton, sediments) should eventually be made available and associated to this overall GRIL dataset.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.016 |
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".