MétaCan
Menu
Back to cohort
Record W4403114610 · doi:10.1002/csan.21410

Effectiveness of Riparian Zones for Retaining Phosphorus

2024· article· en· W4403114610 on OpenAlexaboutno aff

Bibliographic record

VenueCSA News · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Water Nutrient Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsRiparian zonePhosphorusEnvironmental scienceGeologyEcologyChemistryBiologyHabitat

Abstract

fetched live from OpenAlex

High phosphorus inputs to fresh surface waters can lead to harmful algal blooms and deterioration of aquatic ecosystems. Vegetated riparian zones are widely promoted to reduce pollutants, including phosphorus (P), from entering streams by trapping sediments and promoting uptake of nutrients by plants. But it is still unclear how effective these zones are because P that gets trapped may later be released and transported to the stream. To explore the role of the shallow aquifer in retaining and releasing P from riparian zones, researchers from Western University and Environment Climate Change Canada conducted a 19-month field study that measured P concentrations and potential controlling factors in a shallow riparian aquifer. Results showed infrequent high concentrations of P that appeared random and short lived (referred to as hot moments) without any one dominant controlling factor. This illustrates the complexity of P behavior in riparian groundwater and shows that caution is needed in assessing the impacts of groundwater on stream water quality when monitoring locations are distant to the stream. The findings will help scientists and managers draw more informed conclusions from monitoring data on the effectiveness of these best management practices. Top: Aerial image of a monitoring transect between an agricultural field and stream to examine the fate of phosphorus in the subsurface of a riparian zone. Bottom: Field equipment transect across a riparian zone in Kintore Creek, Ontario, to monitor phosphorus release and retention in the shallow aquifer. Photos by Shuyang Wang, Western University.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.556
Threshold uncertainty score0.236

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)0.0000.000

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 head0.010
GPT teacher head0.241
Teacher spread0.230 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCSA NewsSame topicSoil and Water Nutrient DynamicsFrench-language works237,207