Effective Method To Determine Supersaturation of Tar Balls Deposited along the Caspian Sea
Bibliographic record
Abstract
The south coast of the Caspian Sea is being faced with oil pollution because of intensive petroleum activities in the region. Stranded tar balls on the beaches are considered as one of the evidence for oil pollution. In this study, the supersaturation of tar balls, which are collected from Caspian Sea beaches, is investigated using the anti-solvent precipitation technique. The scope of this research is the metastable zone width limit and its influence on supersaturation. Supersaturation is measured for precipitated tar ball particles within a n -hexane/methanol mixture. In general, supersaturation acts as a driving force for tar ball precipitation when the anti-solvent is added. Response surface methodology is used to evaluate the influences of vital parameters, such as anti-solvent addition rate, mixing regime, initial solute concentration, and metastable zone, on the supersaturation phenomenon, leading toward obtaining a statistical model to forecast supersaturation. In comparison of the response surface model predictions to the experimental data, a very good accuracy is noticed. Moreover, the analysis of variance (ANOVA) technique is employed to evaluate validity of the proposed model, implying that the metastability zone width has the most important effect on the supersaturation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".