MétaCan
Menu
Back to cohort
Record W2025734507 · doi:10.1002/hyp.1356

Phosphate retention in an agricultural stream using experimental additions of phosphate

2003· article· en· W2025734507 on OpenAlexafffundabout
Merrin L. Macrae, Michael English, Sherry L. Schiff, Micheal Stone

Bibliographic record

VenueHydrological Processes · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversity of WaterlooWilfrid Laurier University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhosphateEnvironmental sciencePhosphorusHydrology (agriculture)Vegetation (pathology)Perennial streamStream bedSedimentChemistrySTREAMSGeologyGeomorphology

Abstract

fetched live from OpenAlex

Abstract In‐stream experiments involving additions of phosphate were conducted to determine the soluble reactive phosphorus (SRP) retention potential of a perennial first‐order stream that drains a 2·7 km 2 agricultural catchment in southern Ontario. SRP retention was determined in relation to highly elevated SRP concentrations under low flow conditions. Point source treatments of phosphate were added to three reaches of this stream during two time periods when baseline SRP concentrations were notably different (early summer and early autumn). The reaches selected varied with respect to streambed shape and gradient, direction of groundwater flow, and channel vegetation type and density. One of the three experimental stream reaches was dredged between the two sampling periods, so that all vegetation and the top 25 cm of sediments were removed. SRP retention in the stream ranged from 0·8 to 24·1 µg m −2 s −1 . Dredging the stream sediments did not alter the ability of the stream to remove SRP from the water column. SRP retention over the experimental reaches was generally 5–10% of the elevated concentration (0·7–4·2 mg l −1 , a factor of 8–53 above pre‐experiment conditions), although low‐flow conditions in the stream were conducive to retention by stream sediments. Copyright © 2003 John Wiley & Sons, Ltd.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.589
Threshold uncertainty score0.577

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.247
Teacher spread0.221 · 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

Citations28
Published2003
Admission routes3
Has abstractyes

Explore more

Same venueHydrological ProcessesSame topicSoil and Water Nutrient DynamicsFrench-language works237,207