Prediction of Organic Pollutants Transfers in Contaminated Soils Using an In-House Thermodynamic Calculator
Bibliographic record
Abstract
Regulations are becoming stricter and stricter regarding soil pollutions.At the same time the amount of money available to investigate the risks associated with soil pollutions remains limited.This paper proposes to investigate the transfer of pollutants by means of a quick running numerical tool.Indeed, this numerical tool based on equations of state has been developed to compute vapor-liquid or vapour-liquid-liquid thermodynamic equilibrium as a function of temperature and pressure.It has been specifically adapted for the study of organic pollutants in contaminated soils.This tool requires chemical information from the contaminated soil sample as input data.The soil must therefore be analysed to determine its characteristics and the total concentrations of pollutants.This information is used to calculate the overall mole fractions (zi) of each pollutant in the contaminated soil.These mole fractions are thus the input data for the numerical tool to perform the components or pollutants distribution calculation.The results of this tool, which is called Transfero-Pol, therefore depend on the input data obtained from the analysis of the contaminated soil.These results make it possible to understand the long-term distribution of pollutants within the different phases of the soil as a function of temperature.In this paper, the theory behind the numerical tool Transfero-Pol is derived.A case study is thus presented to assess the results from Transfero-Pol by comparing them to the outputs of a commercial software.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".