MétaCan
Menu
Back to cohort
Record W2283940387 · doi:10.2166/wqrjc.2015.132

Groundwater nitrate and chloride trends in an agriculture-intensive area in southern Alberta, Canada

2015· article· en· W2283940387 on OpenAlexafffundabout
Jacqueline Köhn, David X. Soto, Mike Iwanyshyn, Barry M. Olson, Andrea R. Kalischuk, Kristen Lorenz, M. Jim Hendry

Bibliographic record

VenueWater Quality Research Journal · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of SaskatchewanUniversity of New BrunswickAlberta Ministry of Agriculture and Forestry
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Agriculture and ForestryUniversity of Saskatchewan
KeywordsGroundwaterPiezometerNitrateEnvironmental scienceWater tableHydrology (agriculture)TransectIrrigationSoil waterAgricultureManureWater qualityIntensive farmingChlorideAquiferSoil scienceGeologyGeographyAgronomyEcologyChemistry

Abstract

fetched live from OpenAlex

The potential effect of manure management from livestock production on groundwater quality is an issue of concern. Groundwater sampling from a regional transect in southern Alberta, Canada, was conducted to determine changes in groundwater quality with time. The study area has extensive irrigation and a high density of confined feeding operations. Nitrate-N (NO3−-N) and chloride (Cl−) concentration data from 23 groundwater-study wells were evaluated from 1994 to 2014. Twelve of these wells were water-table wells and 11 were piezometers. Of the 23 wells, 14 had significant temporal trends (increasing or decreasing) for NO3−-N and/or Cl− concentrations. On a regional basis, NO3−-N increased slightly with time while Cl− changed very little, suggesting that the effects of agricultural activities on regional groundwater quality have generally remained constant. However, concentration changes occurred on a smaller scale. Shallow groundwater in coarse-textured soils is at a relatively higher risk of contamination than groundwater in fine-textured soils, especially in locations where intensive agricultural activities occur.

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)0.000
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.109
GPT teacher head0.320
Teacher spread0.211 · 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

Citations47
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueWater Quality Research JournalSame topicGroundwater and Isotope GeochemistryFrench-language works237,207