How will climate change influence phosphorus systems? -- an expert elicitation workshop
Bibliographic record
Abstract
Phosphorus (P) sustainability is a ‘wicked problem’ due to environmental and societal challenges. This may be further exacerbated by climate change, although within-discipline literature is lacking. Our knowledge of how climate change may impact P dynamics is challenged by our understanding of baseline vs managed system response signals, complex biophysical and societal process interactions and thresholds. This challenge was discussed at the 10th International P Workshop, where experts from academia and industry gathered. We asked international experts to discuss the impacts of climate change on P across three topics: soils, waters, and human systems. Participants discussed knowledge and data availability on these topics, and positioned their responses on a matrix with importance (relevance of the impact according to their understanding and knowledge) and confidence (about the data availability regarding that impact) as axes. The 320 statements were digitized and categorized into themes, and we report them in context with the current literature. Our analysis emphasizes that phosphorus must become more visible in climate change discourse and addressed through transdisciplinary approaches. We recommend further data collection regarding circular economy and climate adaptation, as well as modelling and policy development to anticipate risks and support adaptive P management. This includes attention to thresholds and socio-economic linkages, where consequences of inaction may be abrupt and widespread. We provide suggestions for research and practice, including forthcoming International Phosphorus Workshops.
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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 | 0.015 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".