Phosphorus Non-Point Pollution from Equestrian Wastes and the Need for Recycling
Bibliographic record
Abstract
South Florida and much of the rest of the World suffers from harmful algal blooms (HABs) and controls of both nitrogen (N) and phosphorus (P) pollution are required to curtail the onset, spread and/or expansion of these blooms. This report covers our studies on several aspects of equestrian waste (viz. horse manure) aimed at yielding an overview of phosphorus and its pollution stemming from non-point horse manure sources in portions of Palm Beach County Florida. Methods included a modified Hedley extraction sequence, emphasizing ‘easily extractable phosphorus’ (EEP), and 31P nuclear magnetic resonance (NMR) spectroscopic identification of organic phosphorus (Po) species. Samples included fresh and aged horse manure, pasture soils, horse feed and pasture grasses, and canal waters adjacent to equestrian or agricultural fields. Easily extractable Phosphorus (EEP) averaged about 54-77% of the total horse manure phosphorus. Total phosphorus ranged from 13,020 – 22,300 mg per kilogram dry weight. (≈60-100 lbs. P2O5 / ton and on a wet weight basis, this equates to 4,000 to 14,818 grams-P/ U.S. ton or 8.8 to 32.6 pounds of phosphorus (≈ 20-75 lb. P2O5) per wet weight ton of horse manure. Considering the values of EEP in fresh samples from a single horse, we found a range of 8,000 – 17,000 mg-P/kg (8-17 g-P/kg) dry weight horse manure. Soil samples yielded the highest P in the NaOH extract of the Hedley sequence. This equates to the Al, Fe and ester forms. Phosphorus (viz. EEP) runoff is viewed here as a non-point P pollution source.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".