Dataset for the assessment of metallic pollution in the Saint-Charles River sediments (Québec City, QC, Canada)
Bibliographic record
Abstract
This Data in Brief article presents sedimentological and geochemical parameters from a set of sedimentary samples collected in the Saint-Charles River, a tributary of the Saint-Lawrence River flowing in Québec City (QC, Canada). It details the experimental design, methods, materials and results of destructive analyses related to a multi-proxy study of polymetallic contamination in sediments collected within an urban reservoir (Spatial and temporal patterns of metallic pollution in Québec City, Canada: Sources and hazard assessment from reservoir sediment records, https://doi.org/10.1016/j.scitotenv.2019.04.021, (Chassiot et al., 2019)). The present article summarizes the results of relevant parameters on a set of 68 samples: total organic carbon (TOC), sulfur content, grain-size, and concentrations of heavy and trace metals. It also presents the calculation of enrichment factors, geoaccumulation indexes, and metallic pollution index.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".