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Record W4391117413 · doi:10.1039/d3em00506b

Emerging investigator series: impacts of land use on dissolved organic matter quality in agricultural watersheds: a molecular perspective

2024· article· en· W4391117413 on OpenAlexafffund
A. Sethumadhavan, Tao Liang, Vaughn Mangal

Bibliographic record

VenueEnvironmental Science Processes & Impacts · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine and coastal ecosystems
Canadian institutionsToronto and Region Conservation AuthorityBrock University
FundersNatural Sciences and Engineering Research Council of CanadaBrock University
KeywordsDissolved organic carbonBiogeochemical cycleEnvironmental scienceWater qualityAgricultureOrganic matterLand useAgricultural landEnvironmental chemistryScale (ratio)Aquatic ecosystemEcologyGeographyChemistryBiology

Abstract

fetched live from OpenAlex

). In addition, watersheds with higher phosphorus concentrations had more negative nominal oxygenation state of carbon (NOSC) values, suggesting biologically processed DOM correlating with increased phosphorus levels in aquatic systems. To relate the molecular properties of DOM to landscape metrics, we used Spearman's correlation analysis to reveal that agriculturally impacted and community developments enhanced the molecular signature of unsaturated hydrocarbon. In addition, we identified 65 dissolved organic phosphorus (DOP) molecules that significantly increased in abundance with disturbance, likely due to microbial mineralization of existing DOM with the addition of phosphorus to form larger, biologically inaccessible molecules. The overall recalcitrance of the identified molecules can serve as molecular signatures when evaluating the level of disturbance of a watershed.

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)0.004
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.082
Threshold uncertainty score0.274

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0820.012

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 head0.008
GPT teacher head0.220
Teacher spread0.213 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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