Correction of profiles of in‐situ chlorophyll fluorometry for the contribution of fluorescence originating from non‐algal matter
Bibliographic record
Abstract
Abstract In situ chlorophyll fluorometers have been widely employed for more than half a century, and to date, it still remains the most used instrument to estimate chlorophyll‐a concentration in the field, especially for measurements onboard autonomous observation platforms, e.g., Bio‐Argo floats and gliders. However, in deep waters (> 300 m) of some specific regions, e.g., subtropical gyres and the Black Sea, the chlorophyll fluorescence profiles frequently reveal “deep sea red fluorescence” features. In line with previous studies and through the analysis of a large data set (cruise transect in the South East Pacific and data acquired by 82 Bio‐Argo floats), we show that the fluorescence signal measured by a humic‐like DOM fluorometer is highly correlated to the “deep sea red fluorescence.” Both fluorescence signals are indeed linearly related in deep waters. To remove the contribution of non‐algal organic matter from chlorophyll fluorescence profiles, we introduce a new correction. Rather that removing a constant value (generally the deepest chlorophyll a fluorescence value from the profile, i.e., so‐called “deep‐offset correction”), we propose a correction method which relies on DOM fluorometry and on its variation with depth. This new method is validated with chlorophyll concentration extracted from water samples and further applied on the Bio‐Argo float data set. More generally, we discuss the potential of the proposed method to become a standard and routine procedure in quality‐control and correction of chlorophyll a fluorescence originating from Bio‐Argo network.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".