TWO WAVELENGTHS MODEL METHOD FOR DETERMINATION OF OIL EMULSION DEPTH ON THE SEA SURFACE IN INFRARED BAND
Bibliographic record
Abstract
The measurement of oil slick thickness has long been a relative science, with absolute measurement eluding the remote sensing community. Knowledge of oil slick thickness would significantly benefit both the spill response and scientific research communities. The effective direction ofoil spill countermeasures such as in situ burning and dispersant application depends on knowledge of slick thickness and volume. Without accurate thickness information, application ofthese technologies in a spill response is a hit-and-miss scenario at best. In the research community there remains a great deal to be learned about oil slick spreading, and dispersant effectiveness. A remote sensor which can provide an absolute measurement of oil thickness, could begin to unravel some of the mystery surrounding the dynamics of oil slick spreading and provide a real method of measuring dispersant effectiveness. One ofthe most exciting roles for the slick thickness measurement sensor would be the calibration of other pieces of remote sensing equipment. Some of the most economical and commonly used airborne remote sensors are the ultraviolet/infrared (UV/IR) scanners and cameras. The UV portion of the sensor responds to the entire area ofthe slick including the thin sheen areas. The thermal IR portion of the sensor provides information on the thicker portions of the slick. The integration of data from the UV and IR provides an indication ofthe thick and thin portions of the slick. This is however a relative picture with little known about the actual thicknesses involved. Calibration ofthese economical instruments with a sensor capable of absolute slick thickness measurement would be a boon to the response community.
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 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 | 0.000 |
| 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) | 0.000 | 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".