Factors Affecting the Efficiency of Carbon Monoxide Photoproduction in the St. Lawrence Estuarine System (Canada)
Bibliographic record
Abstract
This study examined the effects of water temperature and the origin (terrestrial vs marine) and light history of chromophoric dissolved organic matter (CDOM) on the apparent quantum yields of carbon monoxide (CO) photoproduction for water samples collected along a salinity gradient (salinity range: 0-33) in the St. Lawrence estuarine system (Canada). The solar insolation-weighted mean apparent quantum yield of CO (phico) decreased as much as fourfold with increasing salinity and showed a strong positive correlation with the dissolved organic carbon-specific absorption coefficient at 254 nm. This suggests that terrestrial CDOM is more efficient at photochemically producing CO than is marine algae-derived CDOM and that aromatic moieties are likely involved in this photoprocess. CDOM photobleaching, mainly at the very early stage, dramatically decreased phico (by up to 6.4 times) for low-salinity samples, but photobleaching had little effect on the most marine sample. For a 20 degree C increase in temperature, phico increased by approximately 70% for low-salinity samples and 30-40% for saline samples. This study demonstrates that water temperature, as well as the CDOM's origin and light history, strongly affect the efficiency of CO photoproduction. These factors should be taken into account in modeling the photochemical fluxes of CO and other related CDOM photoproducts on varying spatiotemporal scales.
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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) | 0.000 | 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".