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Record W2107010647 · doi:10.4141/cjss09088

A modified Ontario P index as a tool for on-farm phosphorus management

2011· article· en· W2107010647 on OpenAlexaffvenueabout
Keith Reid

Bibliographic record

VenueCanadian Journal of Soil Science · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsMinistry of Agriculture, Food and Rural Affairs
Fundersnot available
KeywordsIndex (typography)Environmental sciencePhosphorusManureSurface runoffAgricultural engineeringFertilizerHydrology (agriculture)Risk managementEnvironmental engineeringComputer scienceBusinessAgronomyEngineeringEcologyChemistryGeotechnical engineering

Abstract

fetched live from OpenAlex

Reid, D. K. 2011. A modified Ontario P index as a tool for on-farm phosphorus management. Can. J. Soil Sci. 91: 455–466. The phosphorus index (P index) concept has gained wide acceptance as a tool to aid users in reducing P losses to surface water, but there is a wide range of potential users with differing goals on what the P index could or should do. To effectively change the behaviour of farmers who are applying fertilizer or manure to their fields requires a change in focus of the P index. This paper proposes a modified P Index that assesses the inherent risk of P loss to surface water from a given area separately from the risk of P loss from applied materials, to make it easier for farmers to use the output from the P index for management decisions. It also incorporates changes in index calculations to incorporate the risk of P losses through tile drains as well as overland flow, to reflect the differences in transport of dissolved versus particulate P, and to match the weightings of the P index factors to the relative risk of P loss from the landscape under Ontario conditions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.721
Threshold uncertainty score0.555

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.017
GPT teacher head0.199
Teacher spread0.181 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2011
Admission routes3
Has abstractyes

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