Sulfide production kinetics and model of stormwater retention ponds
Bibliographic record
Abstract
Abstract Stormwater retention ponds can play a critical role in mitigating the detrimental effects of urbanization on receiving waters that result from increases in polluted runoff. However, the benthic oxygen demand of stormwater facilities may cause significant hypoxia and trigger the production of hydrogen sulfide (H2S). This process is not well-documented and further research is needed to characterize benthic processes in stormwater retention ponds in order to improve their design and operation. In this study, sediment oxygen demand (SOD), sediment ammonia release (SAR) and sediment sulfide production (SSP) kinetics were characterized in situ and in the laboratory. In situ SOD and SSP data were utilized to develop a stormwater retention pond water sulfide concentration model which demonstrates strong correlation with sulfide concentrations observed in situ (r = 0.724, N = 91, p < 0.001) and in laboratory experiments (r = 0.691, N = 38, p < 0.001). At 4 °C, in situ rates of SOD, SAR and SSP were higher than those measured in laboratory. Sulfate-reducing bacteria (SRB) represented 4.99% of the bacteria present in the top 30 cm of the pond sediment, with Desulfobulbaceae spp., Desulfobacteraceae spp. and Desulfococcus spp. being the dominant SRB taxa identified.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".