Decomposition study of in vivo phytoplankton absorption spectra aimed at identifying the pigments and the phytoplankton group in complex case 2 coastal waters of the Arabian Sea
Bibliographic record
Abstract
Abstract Phytoplankton modify the optical properties of the seawater by altering the subsurface light field. Information on the accessory pigments present in the phytoplankton helps to differentiate major phytoplankton classes or taxonomic groups. The variability in the absorption spectra of phytoplankton and particulate matter of case 2 coastal waters of the Southeastern Arabian Sea were studied from June 2010 to November 2011. The phytoplankton specific absorption coefficient, at 440 nm and 675 nm, a*ph (440) and a*ph (675) varied from 0.018 to 0.32 m2 mg-1 and from 0.0005 to 0.16 m2 mg-1, respectively. The 4th derivative spectra computed for each in vivo absorption spectrum showed that the amplitude of maxima obtained is proportional to the concentration of the chromoprotein which absorbed that wavelength. Regression of pigment concentration against the 4th derivative spectral coefficient showed that the measurements of particulate absorption could provide quantitative information on chlorophyll α and other accessory pigment concentrations. Fucoxanthin and diadinoxanthin, the carotenoid pigments found in the diatoms were identified from the derivatives peaks. The study demonstrates the utility of using the 4th derivative analysis as a tool to identify the dominating phytoplankton group and its pigment composition.
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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 | 0.000 |
| 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 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".