Monitoring red tide with oceanic surface spectrum measured in maritime observatory
Bibliographic record
Abstract
This article distinguishes the different characteristics between the clean water body and red tide using the reflection coefficient value of oceanic surface spectrum as well as the data of chlorophyll and silt density observed by 56 observation stations in Vancouver sea area of Canada,east coast of Pacific Ocean,and investigates the changeable tendency of the reflection coefficient value of oceanic surface spectrum when the red tide takes place.Through the comparison of different groups of datum,it is found that the fluorescent peak of 685nm wavelength of reflection coefficient value of oceanic surface spectrum in the clean water body shifts to 710nm red optical wavelength when the red tide takes place in the clean water body.The average value of the reflection coefficient of oceanic surface spectrum in clean water body is bigger than that in the red tide for wavelength between 400nm and 588nm,but the former is smaller than that of the latter after 588nm wavelength.At 688nm wavelength the values of reflection coefficient for both the clean water body and red tide water body are the same(about,0.25).The average value of the reflection coefficient of oceanic surface spectrum of red tide water body between 688 nm and 756 nm is bigger than that of the clean water body.The wavelength difference of fluorescent peaks between clean water body and red tide water body and their different characteristics of spectrum reflectivity can be used to select the best band for monitoring red tide.
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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 | 0.001 |
| 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 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".