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Record W4405200326 · doi:10.1002/jeq2.20659

Toward a transdisciplinary and unifying definition of legacy phosphorus

2024· article· en· W4405200326 on OpenAlexaff
Amy L. Shober, Zachary Simpson, Helen P. Jarvie, Merrin L. Macrae, Peter J. A. Kleinman, P. M. Haygarth, Stephanie B. Kulesza, Luciano Colpo Gatiboni, J. Davies

Bibliographic record

VenueJournal of Environmental Quality · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversity of Waterloo
FundersUniversity of Warwick
KeywordsPhosphorusEnvironmental scienceEnvironmental planningChemistry

Abstract

fetched live from OpenAlex

Legacy phosphorus (P) is a concept advanced by Dr. Andrew Sharpley and colleagues that was originally applied to the persistence of anthropogenic signatures in watersheds, and it has since been adopted in a diversity of settings to help guide the science and management of P. Following Sharpley's example to develop consensus-based science, we considered contrasting perspectives on legacy P and defined legacy P as those stores within the environment that arise from historic human activity excluding "natural" or "background" geogenic sources. Legacy P is not restricted to one system or setting; it may reside in soils, sediments, biota, and water bodies. Legacy P has been estimated by fluxes (inputs minus outputs of P to a system) or, equivalently, by mass stocks (total minus geogenic). Because the origin of P in the environment cannot currently be directly quantified, we recommend that researchers report "total P" to track wider watershed P stocks and fluxes of P that include legacy P. We recognize that the definition of legacy P will continue to evolve as we continue to work toward consensus. Ultimately, the final definition of legacy P has consequences for the implementation and success of regulatory and voluntary strategies for legacy P management in agricultural systems. We support continued progress toward a consensus-backed, research-grounded definition for legacy P that is widely applicable yet useful for guiding management and policy.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.037
GPT teacher head0.272
Teacher spread0.235 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations12
Published2024
Admission routes1
Has abstractyes

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