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How will climate change influence phosphorus systems? -- an expert elicitation workshop

2025· preprint· en· W4414145223 on OpenAlexaff
Camilla Negri, Golnaz Ezzati, P. M. Haygarth, Merrin L. Macrae, Per‐Erik Mellander, Bryan M. Spears, Sara Trojahn, Johanna Wandel, Marc Stutter

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicPhosphorus and nutrient management
Canadian institutionsUniversity of WaterlooToronto Metropolitan University
Fundersnot available
KeywordsClimate changeContext (archaeology)Expert elicitationSustainabilityBaseline (sea)Process (computing)Global warmingEffects of global warming

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.269
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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