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Relationship of Soil Organic Matter Characteristics to Organic Contaminant Sequestration and Bioavailability

2000· article· en· W2055981117 on OpenAlexfundno aff
Angela D. Lueking, Weilin Huang, Sara B. Soderstrom, Minsun Kim, Walter J. Weber

Bibliographic record

VenueJournal of Environmental Quality · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsnot available
FundersMcGill UniversityUniversity of KansasMichigan State UniversityUniversity of MichiganOffice of Research and Development
KeywordsOrganic matterEnvironmental chemistrySorptionDesorptionChemistryPhenanthreneBioavailabilityMineralization (soil science)Soil organic matterSoil waterAdsorptionSoil scienceEnvironmental scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Sorption and desorption equilibria of phenanthrene with respect to three different types of geosorbents were measured, as were the rates of desorption and biological mineralization of this representative hydrophobic organic contaminant. The chemical nature of the organic matter associated with each geosorbent was characterized using solid state 13C‐NMR spectrometry. The results of these studies reveal that both the desorption behavior and the microbial bioavailability of the sorbed contaminant are influenced by the physicochemical character of the organic matter. The more reduced and condensed the organic matter, the greater the extent of sorption‐desorption hysteresis, the slower the desorption rate, and the less readily bioavailable the sorbed contaminant. These observations are consistent with projections predicated on a dual reactive domain model introduced earlier to describe the sorptive reactivities of different types of soil/sediment organic matter with hydrophobic organic contaminants.

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)0.001
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.014
GPT teacher head0.238
Teacher spread0.223 · 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

Citations72
Published2000
Admission routes1
Has abstractyes

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