Validation of Non-photochemical Quenching Corrections for Chlorophyll-a Measurements Aboard Ships of Opportunity
Bibliographic record
Abstract
The in vivo fluorescence of chlorophyll- a is commonly used as a proxy for phytoplankton biomass. Measurement of in vivo fluorescence in the field is attractive because it can be made at high spatial temporal, and vertical resolution relative to discrete sampling and pigment extraction. Fluorometers installed on ships of opportunity provide a cost-effective alternative to many of the traditional sampling methods. However, fluorescence-based estimates of chlorophyll- a can be impacted by sensor calibration and biofouling, variations in phytoplankton taxonomy and physiology (such as non-photochemical quenching) and the influence of other fluorescing matters in the water. Several methods have been proposed to address these issues separately, but few studies have addressed the interaction of multiple sources of error in the in vivo Chl- a fluorescence signal. Here, we demonstrate a method to improve the accuracy of chlorophyll- a concentration retrieved from a coastal ferry system, operating in a dynamic estuarine system. First, we used HPLC chlorophyll- a measurements acquired in low-light conditions to correct sensor level bias. Next, we tested three methods to correct the effect of non-photochemical quenching and evaluated the accuracy of each method using HPLC. As our study area is in highly dynamic coastal waters, we also evaluated the accuracy of our correction procedure across a range of irradiance and biogeochemical conditions. We found that sensor bias accounted for a significant portion of error in the fluorescence signal. The NPQ correction developed by Davis et al. (2008) best improved correspondence between in vivo Chl- a fluorescence and HPLC-based measurement of extracted Chl- a . We suggest the use of this correction for in vivo Chl-a measurements along with pre-processing steps to correct potential sensor biofouling and bias.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".