Hazardous and Noxious Substance detection by hyperspectral imagery for marine pollution application
Bibliographic record
Abstract
In this paper we show that hyperspectral imaging systems are able to improve the detection and characterization Hazardous and Noxious Substances (HNS) in the case of marine pollution. We analysed nadir hyperspectral image acquisitions from 0.4 to 12μm corresponding to two different campaigns: (i) HNS release at the surface of a sea water pool; (ii) HNS release in the Mediterranean Sea under real conditions where hyperspectral sensors were used in addition to existing systems operationally deployed for coastal survey. The spatial and temporal evolution of hyperspectral data of HNS slicks acquired from these campaigns show that hyperspectral imaging from visible to longwave infrared is sensitive to various chemical products due to their refractive index, specific absorption, or evaporating gas. Such sensitivity seems to be very helpful to identify the various products or to collect evidence of chemical pollution.
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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 | 0.000 |
| 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".