Evolution of tar ball aggregates in Caspian Sea: Implications of connectionist tools linked with image analysis
Bibliographic record
Abstract
Intensive petroleum activities in the Caspian Sea lead to a considerable oil pollution in the various parts of this region. Tar balls stranded on the south coast of the Caspian Sea evidently cause (and accelerate) this serious environmental threat. In this study, a poloxamine copolymeric surfactant is used to agglomerate tar balls in a system containing the solvent, antisolvent, and Caspian Sea tar balls. This research work is conducted to determine the cumulative size distribution of tar balls in the presence of a poloxamine copolymer. The evolution of tar ball aggregates is experimentally and theoretically investigated and the steady‐state size distribution of the tar balls is estimated by Back Propagation Artificial Neural Network (ANN‐BP) and a new evolutionary algorithm, called Imperialist Competitive Algorithm (ICA). The average size of tar ball aggregates increases with time and reaches a maximum value. It then declines and eventually approaches a steady‐state condition so that an almost constant size for the particles is attained. The statistical analysis shows a good agreement between the predicted values (obtained by the neural network combined with ICA, known as ANN‐ICA) and the real data for the steady‐state size distribution of the aggregates. This research study can considerably help to further understand evolution of tar balls (that hit the beaches) and consequently to find effective and economical ways for tar balls removal from the beaches. © 2018 American Institute of Chemical Engineers Environ Prog, 37: 1901–1907, 2018
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".