Bibliographic record
Abstract
ABSTRACT Oil associates with fine mineral particles in an aqueous medium not only as molecules adsorbed on mineral surfaces, but also as a discrete phase to form microscopic oil-mineral aggregates (OMA). On the basis of recent studies, OMA formation now is believed to be instrumental in the natural recovery of oil spill impacted shorelines and in the efficacy of cleanup techniques such as surf washing. A better understanding of the nature and properties of OMA will help predict the fate of oil spilled in the aquatic environment. This work describes the various instruments and methods currently available for the detection and identification of OMA. Three types of OMA have been characterized by microscopy techniques: droplet, solid, and flake aggregates. Droplet aggregates are oil droplets (usually a few μm in diameter) surrounded by individual or aggregated mineral particles. Solid aggregates are a mixture of oil and minerals blended into microscopic bodies of various shapes. Flake aggregates are thin sheets reaching several millimeters across in which mineral and oil are arranged in a regular pattern. Energy from breaking waves facilitates OMA formation. Once formed, OMA appear to be very stable structures the buoyancy of which depends on the ratio of oil to mineral in each individual aggregate.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".