Evaluating\nthe Impact of Legacy P and Agricultural\nConservation Practices on Nutrient Loads from the Maumee River Watershed
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The\nrecent resurgence of hypoxia and harmful algal blooms in Lake\nErie, driven substantially by phosphorus loads from agriculture, have\nled the United States and Canada to begin developing plans to meet\nnew phosphorus load targets. To provide insight into which agricultural\nmanagement options could help reach these targets, we tested alternative\nagricultural-land-use and land-management scenarios on phosphorus\nloads to Lake Erie. These scenarios highlight certain constraints\non phosphorus load reductions from changes in the Maumee River Watershed\n(MRW), which contributes roughly half of the phosphorus load to the\nlake’s western basin. We evaluate the effects on phosphorus\nloads under nutrient management strategies, reduction of fertilizer\napplications, employing vegetative buffers, and implementing widespread\ncover crops and alternative cropping changes. Results indicate that\neven if fertilizer application ceased, it may take years to see desired\ndecreases in phosphorus loads, especially if we experience greater\nspring precipitation or snowmelt. Scenarios also indicate that widespread\nconversions to perennial crops that may be used for biofuel production\nare capable of substantially reducing phosphorus loads. This work\ndemonstrates that a combination of legacy phosphorus, land management,\nland use, and climate should all be considered when seeking phosphorus-loading\nsolutions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 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 it