ON THE OIL-MINERAL AGGREGATION PROCESS: A PROMISING RESPONSE TECHNOLOGY IN ICE-INFESTED WATERS
Bibliographic record
Abstract
ABSTRACT The oil-mineral aggregation (OMA) process refers to oil droplets and fine sediments interaction leading to the formation of small aggregates. Previous studies have highlighted the significance of oil-mineral aggregate formation on the persistence of oil and the feasibility of its use for the development of spill countermeasures. However, the efficiency of the OMA process in ice-infested waters is not well known. Some preliminary laboratory works have reported promising results regarding the aggregation process in the presence of ice. In the light of these results, the Canadian Coast Guard has conducted a research program that aims to elaborate clean-up measures using the OMA process in ice-infested waters. The oceanographic parameters likely to affect the efficiency of the OMA process were reviewed with respect to the oceanographic conditions prevailing in the Saint-Lawrence River during winter. These results suggested that the low turbidity values and water turbulence prevailing during icing periods are likely to be important parameters influencing oil dispersion efficiency by the OMA process. Clean-up measures which would overcome these limiting effects, based on laboratory and field tests would be developed. This paper presents a literature review on the international expertise related to oil-ice-sediment interactions. The efficiency of the OMA process and the feasibility of using OMA as an oil spill countermeasure strategy in ice-infested waters is discussed herein. The main objectives of the experimental protocol designed for the development of clean-up measures aimed at enhancing oil dispersion in ice-infested waters by the OMA process are presented.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".