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Record W2536119694 · doi:10.3934/environsci.2016.4.663

2,4-D and Glyphosate affect aquatic biofilm accrual, gross primary production, and community respiration

2016· article· en· W2536119694 on OpenAlexaffabout
Lawton Shaw, Ahmad Mibbayad

Bibliographic record

VenueAIMS environmental science · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsAthabasca University
Fundersnot available
KeywordsAffect (linguistics)AccrualBiofilmGlyphosateRespirationProduction (economics)Primary (astronomy)Environmental scienceEcologyBiologyBusinessBotanyPsychologyEconomicsBacteriaAccountingCommunication

Abstract

fetched live from OpenAlex

2,4-Dichlorophenoxyacetic acid (2,4-D) and glyphosate are widely used agricultural herbicides commonly found in surface waters near cultivated land. Field experiments were conducted to determine the effects of 2,4-D and glyphosate on biofilms in a pond next to agricultural land in Athabasca, Alberta. Contaminant-exposure substrates (CES) consisted of GF/C glass fiber or a cellulose filter paper substrates placed on specimen jars filled with agar that contained low levels of nitrogen and phosphorus, and different concentrations (15, 9.0, 1.5 mM) of either 2,4-D or glyphosate. Nutrients and herbicide diffused freely through the agar to the substrate surface. CES arrays were deployed 15 cm below the water surface for 22 days, after which biofilms were collected and biomass (chlorophyll <em>a</em>), autotroph gross primary production (GPP), and heterotroph community respiration (CR) were measured. 2,4-D (15 mM) caused significant decreases in rates of biomass accrual (−22%), GPP (−34%), and CR(−63%). Glyphosate (15 mM) also caused significant decreases in rates of biomass accrual (−50%), GPP (−67%), and CR (−47%). For the contaminant concentrations used, mean flux rates are estimated to be between 50–700 ng cm<sup>−2</sup> min<sup>−1</sup>.

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 categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.999

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 studies0.0020.007
Scholarly communication0.0000.001
Open science0.0000.002
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 head0.016
GPT teacher head0.216
Teacher spread0.199 · 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; both teacher heads agree on what is shown here.

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

Citations7
Published2016
Admission routes2
Has abstractyes

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