Behavior of Dissolved Organic Matter in Coral Reef Waters in Relation with Biological Processes
Bibliographic record
Abstract
Behavior of dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) in coral reef waters in relation with biological processes was studied with incubation experiments and field observations in May 2008 and 2009 at the fringing reef of Sesoko Island, Okinawa, Japan. Reef sea water (RSW) and coral mucus added RSW collected from Acropora digitifera (AcrRSW) and Montipora digitata (MonRSW) were incubated for one day in situ and then for 77 days in the laboratory under dark condition. The results indicated that the behavior of DON was different compared to that of DOC in RSW and mucus added (AcrRSW and MonRSW) during dark incubation. Concentration of DON increased from 8.3 µM to 11.8 µM for AcrRSW and 4.0 µM to 15.4 µM for MonRSW during dark incubation period. The increasing rates for DON in AcrSRW and MonRSW were 0.05 µM day-1 and 0.1 µM day-1 respectively. On the other hand DOC concentration decreased from 129.0 µM to 75.0 µM for AcrRSW and 75.1 µM to 64.7 µM for MonRSW, with decreasing rates of 0.7 µM day-1 and 0.1 µM day-1 respectively. We assume that the increase of DON may be determined by difference between rates of inputs of organic matter mainly from mucus and rate of degradation of dissolved organic matter in the water column. These results suggest that recycling of DON is slow than that of DOC in coral reef ecosystem.
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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 | 0.000 |
| 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".