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 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 | 0.051 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".