MétaCan
Menu
Back to cohort
Record W1556928596 · doi:10.4137/aswr.s4471

Sediment and Nutrient Contributions from Subsurface Drains and Point Sources to an Agricultural Watershed

2010· article· en· W1556928596 on OpenAlexaff
B. Ball Coelho, A. J. Bruin, Shawn K. Staton, David Hayman

Bibliographic record

VenueAir Soil and Water Research · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsInro Consultants (Canada)Fisheries and Oceans CanadaAgriculture and Agri-Food Canada
Fundersnot available
KeywordsTile drainageHydrology (agriculture)SedimentWatershedEnvironmental scienceDrainageSurface runoffNutrientSurface waterDrainage basinSoil waterGeologyEnvironmental engineeringSoil scienceEcologyGeomorphologyGeographyBiology

Abstract

fetched live from OpenAlex

Excess sediment and nutrients in surface waters can threaten aquatic life. To determine the relative importance of subsurface drainage as a pathway for movement of sediment and nutrients to surface waters, loading from various tile systems was compared to that from sewage treatment plants (STP) within the same watershed. Movement through tiles comprised 1 to 8% of estimated total (overland plus tile) annual sediment loading from the respective areas drained by the tile. Load during the growing season from five closed drainage systems without surface inlets averaged 5 kg sediment/ha, 0.005 kg dissolved reactive P (DRP)/ha, 0.003 kg NH 4 -N/ha, and 3.8 kg NO 3 -N/ha; and from two open drainage systems with surface inlets averaged 14 kg sediment/ha, 0.03 kg DRP/ha, 0.04 kg NH 4 -N/ha, and 3.1 kg NO 3 -N/ha. The eight STP contributed about 44 530 kg suspended sediments, 3380 kg total P, 1340 kg NH 4 -N, and 116 900 kg NO 3 -N to the watershed annually. Drainage systems added less NH 4 -N and P, but more NO 3 -N and suspended solids to surface waters than STP. Tile drainage pathways for NO 3 -N, STP in the case of P, and overland pathways for sediment are indicated as targets to control loading in artificially drained agricultural watersheds.

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 score0.025
Threshold uncertainty score0.584

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.013
GPT teacher head0.265
Teacher spread0.252 · 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

Citations17
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueAir Soil and Water ResearchSame topicSoil and Water Nutrient DynamicsFrench-language works237,207