The quantitative filter technique for measuring phytoplankton absorption: Interference by MAAs in the UV waveband
Bibliographic record
Abstract
The absorption of suspended particulate material is commonly estimated with the filter pad technique, which requires a correction for pathlength amplification. The pathlength amplification factor (β) varies among different studies and phytoplankton species or communities. It remains the largest source of uncertainty in estimated absorption coefficients. Recently, several empirical models estimating this correction were developed but mostly for visible range. In this study, β was calculated empirically from the ratio of filtered to suspension absorption between 280 and 850 nm for cultures of two dinoflagellates and one diatom. Results show that in the visible waveband, β values are relatively constant and fall within the published range (averages between 2.4 and 2.8). These values remain flat over the UV waveband for the diatom tested. However, below 400 nm, the presence of mycosporine‐like amino acids (MAAs) strongly influences the absorbance measurements. For the dinoflagellates studied, the ultraviolet (UV) absorbance measured on frozen filters (stored in liquid nitrogen) reveals a large peak (Sosik 1999) caused by high concentrations of MAAs, which is much smaller on absorbance scans of suspended cells. This amplified UV peak adds to the true amplification effect caused by the extended pathlength of light in filter pads. This artifact, caused by the extracellular release of the water‐soluble MAAs during freezing, also was observed to a lower degree with measurements performed on fresh filters (immediate scanning), precluding the use of this method to estimate UV absorption in the species tested.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".