Chi-square spectral fitting for concentration retrieval, automatic local calibration, quality control, and water type detection
Bibliographic record
Abstract
In this study, the inverse bio-optical model HYDROPT was calibrated with regional specific inherent optical properties (SIOPs) and various local SIOPs to examine the effect of these calibrations on the retrievals. The study area, the Wadden Sea, is an estuary and tidal flat area with very high concentrations of chlorophyll a (Chl a), suspended particulate matter (SPM), and coloured dissolved organic matter (CDOM). HYDROPT could derive concentrations of Chl a, SPM, and CDOM with a reasonable degree of accuracy when in situ above-water reflectances were used as input (root mean squared error of 0.19–0.52 mg·m−3 for Chl a, 0.28–0.46 mg·m−3 for SPM, and 0.20–0.34 m−1 for aCDOM). However, quality control showed that 70% of the in situ input reflectance spectra were ambiguous; these spectra could be modelled with various sets of SIOPs. Therefore, automatic local calibration based on the spectral fit (χ2) value of the fitting procedure did not necessarily lead to the best results; this was expected to be an advantage of χ2 fitting. When MERIS data were used as input, the concentration maps showed distributions according to the expectations, although tidal flats and nearby land affected the results at the locations that matched with in situ stations. The water types (water with similar SIOPs) that could be detected based on MERIS data were new in this study.
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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 | 0.001 | 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".