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Record W2779660531 · doi:10.1080/02705060.2017.1406873

Change in nutrient loading pattern due to coupled effect of change in concentration and hydroclimatic forces

2017· article· en· W2779660531 on OpenAlexafffundabout
Farshad Shafiei, Philip D. McLoughlin

Bibliographic record

VenueJournal of Freshwater Ecology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsUniversity of Saskatchewan
FundersEnvironment and Climate Change CanadaUniversity of Saskatchewan
KeywordsNutrientEnvironmental scienceFlux (metallurgy)WatershedPrecipitationHydrology (agriculture)Water qualityPhosphorusFlow (mathematics)EcologyChemistryBiologyGeographyGeologyMeteorology

Abstract

fetched live from OpenAlex

Increase in nutrient loading into large riverine systems is of interest because of long-lasting negative effects on downstream water bodies. We know little of this phenomenon with respect to long-term relationships between water quality metrics and trends in river flow. Here we present a multi-decadal (1937–2014) examination of nutrient transport patterns of the South Saskatchewan and Red Deer Rivers, Canada. Using a multi-method approach, we show a slightly increasing trend in annual volume of water reaching a mid-watershed reservoir (Lake Diefenbaker) concomitant with a change in seasonal variability of flow events from the 1970s. Both total nitrogen (TN) and total phosphorus (TP) flux increased during the period of record. TN concentration increased, while TP concentration decreased. On average, the high-flow season transported 95% of annual TP flux, 75% of annual TN flux, and 65% of annual flow. Inter-annually, high-flow season contributions decreased with respect to water volume, but increased with respect to both TN and TP flux. We believe that changes in variability but also timing of hydroclimatic forces are responsible for observed patterns. Higher flows and more precipitation during the high-flow season are most likely responsible for increasing nutrient load regardless of overall decreases in nutrient concentration.

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 score0.420

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 communication0.0000.000
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 head0.020
GPT teacher head0.266
Teacher spread0.246 · 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

Citations4
Published2017
Admission routes3
Has abstractyes

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