Faculty Opinions recommendation of A spatial analysis of phosphorus in the Mississippi river basin.
Bibliographic record
Abstract
Phosphorus (P) in rivers in the Mississippi River basin (MRB) contributes to hypoxia in the Gulf of Mexico and impairs local water quality. We analyzed the spatial pattern of P in the MRB to determine the counties with the greatest January to June P riverine yields and the most critical factors related to this P loss. Using a database of P inputs and landscape characteristics from 1997 through 2006 for each county in the MRB, we created regression models relating riverine total P (TP), dissolved reactive P (DRP), and particulate P (PP) yields for watersheds within the MRB to these factors. Riverine yields of P were estimated from the average concentration of each form of P during January to June for the 10-yr period, multiplied by the average daily flow, and then summed for the 6-mo period. The fraction of land planted in crops, human consumption of P, and precipitation were found to best predict TP yields with a spatial error regression model ( = 0.48, = 101). Dissolved reactive P yields were predicted by fertilizer P inputs, human consumption of P, and precipitation in a multiple regression model ( = 0.42, = 73), whereas PP yields were explained by crop fraction, human consumption of P, and soil bulk density in a spatial error regression model ( = 0.49, = 61). Overall, the Upper Midwest's Cornbelt region and lower Mississippi basin had the counties with the greatest P yields. These results help to point out specific areas where agricultural conservation practices that reduce losses to streams and rivers and point source P removal might limit the intensity or spatial occurrence of Gulf of Mexico hypoxia and improve local water quality.American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. PMID: 21546679
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.176 | 0.046 |
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".