The importance of consensus science to managing phosphorus in the environment: SERA‐17 and the legacy of Andrew Sharpley
Bibliographic record
Abstract
Phosphorus (P) loss from agricultural systems to surface waters, and ultimately, eutrophication, presents a wicked problem requiring transdisciplinary solutions. The mission of SERA-17 (Southern Extension and Research Advisory Information Exchange Group-17) has been to address this problem by developing "Innovative Solutions to Minimize Phosphorus Losses from Agriculture." Over the course of his career, Dr. Andrew Sharpley demonstrated a rare ability to collaboratively achieve consensus around issues related to the science and management of P. The SERA-17 organization served as the central community of experts and stakeholders where that consensus was built and applied. The consensus-based approach, demonstrated by Sharpley and at the core of the SERA-17 organization, was routinely applied to key areas of P science to produce applied outcomes that have been readily adopted: advance foundational science to resolve knowledge gaps and to promote innovation; promote consistency in methods to facilitate comprehensive investigations and conclusions across a diversity of systems; engage diverse stakeholders to prioritize research, and ultimately, ensure that outcomes reflect a plurality of perspectives; and deliver pragmatic solutions that reflect the best information available at a particular time. We review the history of SERA-17 in delivering new science and management recommendations for P, with an eye to elucidating Sharpley's role and legacy in this process.
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How this classification was reachedexpand
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 | 0.005 | 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 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".