Abiotic control of underwater light in a drinking water reservoir: Photon budget analysis and implications for water quality monitoring
Bibliographic record
Abstract
Abstract In optically complex inland waters, the underwater attenuation of photosynthetically active radiation (PAR) is controlled by a variable combination of absorption and scattering components of the lake or river water. Here we applied a photon budget approach to identify the main optical components affecting PAR attenuation in Lake St. Charles, a drinking water reservoir for Québec City, Canada. This analysis showed the dominant role of colored dissolved organic matter (CDOM) absorption (average of 44% of total absorption during the sampling period), but with large changes over depth in the absolute and relative contribution of the individual absorption components (water, nonalgal particulates, phytoplankton and CDOM) to PAR attenuation. This pronounced vertical variation occurred because of the large spectral changes in the light field with depth, and it strongly affected the average in situ diffuse absorption coefficients in the water column. For example, the diffuse absorption coefficient for pure‐water in the ambient light field was 10‐fold higher than the value previously measured in the blue open ocean and erroneously applied to lakes and coastal waters. Photon absorption budget calculations for a range of limnological conditions confirmed that phytoplankton had little direct influence on underwater light, even at chlorophyll a values above those observed during harmful algal blooms in the lake. These results imply that traditional measures of water quality such as Secchi depth and radiometric transparency do not provide a meaningful estimate of the biological state of the water column in CDOM‐colored lakes and reservoirs.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".