Study of Decomposition of Grass and Leaves
Bibliographic record
Abstract
A significant source of nutrient input to water bodies is from grass clippings and leaves (yard debris) washed into drainage systems during storms.Brevard County, Florida, Surface Water Improvement Department conducted a study to determine the nutrient release rates from grass clippings and leaves in order to better understand the chemistry and resultant pollutant loading mechanisms.Sixty-gram samples of mixed freshly cut St. Augustine yard grass (Stenolaphrum secunda/urn) and oak leaves (Quercus sp.) were placed into opaque containers.Coarsely filtered storm/ditch water was added to fill the containers to the S-liter marks.Samples were allowed to go anaerobic, typical of wet sump best management practice (BMP) structure conditions, and tested periodically after soaking and processing.At intervals of: 0, 1, 5, 9, 14, 22, 34, 50, 70, 130, and 180 days, triplicate bucket sets were agitated to simulate mixing from stormwater influx, then poured through a Number 35, US Standard Soil Sieve, and the liquid analyzed.The solids that remained in or on the sieve were analyzed, and the results compared to those of the corresponding liquid phase.The results represent the lawn and leaf litter decomposition and nutrient release rates found in east Florida.This information may be useful in the selection or site design of BMPs for treating nutrients in stonnwater, and determining cleaning frequency.
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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) | 0.001 | 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".