Urban Rainwater Composition Study
Bibliographic record
Abstract
Rainwater samples have been collected in New Orleans from October 1999 to October 2000.The samples were analyzed for water quality parameters: pH, total Kjeldahl nitrogen (TKN), nitrate (N0 3 •), ammonia (NH 3 ) and total phosphorus (TP).Average concentrations show clear seasonal variations with higher levels in spring and summer and lower levels in winter and fall for NO;N and TP.Comparison with Lake Pontchartrain parameters shows that the nitrogen concentration is significantly higher in rainwater than in the lake water but the TP is lower in rainwater than in the lake water.Comparison with agricultural areas (Washington Parish) shows that levels of major species of nutrients from urban areas (Orleans Parish) are higher than those of agricultural areas.The contrasts between urban and agricultural areas can be explained by proximity to the local pollution sources, such as motor vehicles, electric utilities, and petrochemical plants.The findings suggest that for some species of nutrients such as NH 4 +, N0 3 • and POt, anthropogenic emissions are major sources.A strong inverse relationship generally was found between the observed nutrient concentration and the quantity of rainfall in a particular event; however, the total loading due to rainfall increased with increased amounts of rainfall butatadiminishingrate.The rainfall concentrations of ammonia, nitrate/ nitrite and total phosphorus were converted to loads and compared to loads from urban storm runoff loads.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".