Aquatic particulate absorption coefficient combining extraction and bleaching optimized for inland waters
Bibliographic record
Abstract
Abstract The particulate absorption coefficient is one of the fundamental inherent optical properties describing interactions of light with material in water. Its spectral properties contain important information about chemical and biological constituents. It is often partitioned into algal and non‐algal fractions which provide useful information describing phytoplankton. Particulate absorption coefficient has been routinely measured in the ocean particularly to calibrate remote sensing algorithms. However, the methods to measure marine algal and non‐algal absorbing fractions might fail in freshwaters due to difficulties extracting green‐algae pigments and cyanobacterial phycocyanin and the high organic content of the non‐algal particles, making direct bleaching biased. In this work, we describe a method with sequential extraction, bleaching, and post‐processing to obtain unbiased pigments and non‐algal absorption fractions in freshwater environments, and we compare it against the resulting fractions obtained by only extraction or bleaching, using samples collected from 649 lakes across Canada. The resulting non‐algal particles spectra from our method appear free of interfering pigments while maintaining spectral shapes, as verified by the higher correlation coefficient between the 400 and 700 nm exponential coefficient ( S , often referred to as slope) of the non‐algal particles spectra and the organic fraction of total suspended solids, and by having a better correlation between the ratio of absorption coefficient of phytoplankton at 620 and 676 nm and cyanobacterial biomass percentage. Overall, this method solves the two problems in freshwater particulate absorption partitioning associated with (1) unextracted pigments with methanol extraction methods and (2) bias introduced to non‐algal absorption spectra from NaOH bleaching.
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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) | 0.001 | 0.001 |
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".