Datasets of solid and liquid discharges of an urban Mediterranean river and its karst springs (Las River, SE France)
Bibliographic record
Abstract
This data paper presents: (1) the liquid and solid discharge characteristics of the Las River, an urban Mediterranean stream flowing to the Bay of Toulon (south of France), and (2) the water height of the main karst springs supplying the Las River. We assessed the river's discharge with hydrological observations and we explored floods characteristics influencing its solid discharge [1]. The location of the monitoring station near the river's mouth was selected accordingly to accessibility and technical constraints, as far downstream as possible. The vast majority of tributaries (such as possible underground springs, stormwater outlets, urban runoff) were taken into account. A multi-parameter probe (temperature, pressure, turbidity and electric conductivity) and a sediment trap were deployed continuously for 17 months, from October 2012 to March 2014. At the river's sources, probes (temperature, water height) were deployed to characterized karst springs. Times series were averaged at a daily time step, and water height converted in discharge when the rating curve was available. Sediment samples were analyzed for grain-size distribution. Datasets may help to estimate karsts' contributions to the Mediterranean Sea and to assess their influence on rivers discharge and solid yield. Stakeholders may also use the maximum water height to evaluate the flooding risk. Our data also contribute to linking the catchment freshwater to the coastal sea, a connection yet to be fully explored.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".