Seasonal changes of dissolved organic carbon photo-oxidation rates in a tropical humic lagoon: the role of rainfall as a major regulator
Bibliographic record
Abstract
In this paper, we report the seasonal variation of photo-oxidation rates in a tropical humic lagoon and its relation to annual rainfall regime. Photo-oxidation rates ranged from 8.96 to 415.06 µmol C·L–1·day–1, being higher in the beginning to middle of the rainy season and declining throughout the year. Although dissolved organic carbon (DOC) concentration, water color, and sunlight incidence were generally higher in the rainy season, photo-oxidation rates were not significantly related to any of these parameters. Photo-oxidation seems to be influenced mainly by changes in DOC photoreactivity, which was up to threefold higher early in the rainy season, when inputs of fresh allochthonous DOC take place. In the following months, in addition to being continuously degraded by sunlight, DOC is also removed from the water column by processes such as microbial degradation and sedimentation, leading to a decline in DOC concentration and photoreactivity throughout the year until the next rainy season. Thus, the dynamics of DOC inputs caused by the rainfall regime in Comprida Lagoon lead to a yearly pulse of DOC photoreactivity and photo-oxidation rates. We believe this pulse model also fits other aquatic ecosystems subject to similar seasonal inputs of allochthonous DOC, although rainfall would not necessarily be the driving factor.
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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 | 0.001 | 0.000 |
| 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 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".