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Record W2088893083 · doi:10.1021/es001820e

Grass−Air Exchange of Polychlorinated Biphenyls

2001· article· en· W2088893083 on OpenAlexaff
Hayley Hung, Gareth O. Thomas, Kevin C. Jones, D. Mackay

Bibliographic record

VenueEnvironmental Science & Technology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsTrent University
Fundersnot available
KeywordsChemistryPartition coefficientAtmosphere (unit)Environmental chemistryKineticsReaction rate constantAdsorptionAnalytical Chemistry (journal)ChromatographyMeteorology

Abstract

fetched live from OpenAlex

Three field experiments were performed to assess the clearance, uptake, and exchange kinetics of polychlorinated biphenyls (PCBs) between grass and the atmosphere using mixed- and single-species grass (Holcus lanatus). In the clearance experiment, the grass was artificially contaminated by equilibration with diluted Aroclor vapor then exposed to field air, and the rates of depletion were monitored by sampling at regular intervals to determine clearance rate constants. In the uptake experiment, the uptake of PCBs from the ambient atmosphere was followed in growing grass at ambient concentrations for 3 and 6 weeks by analysis of segmented samples along the length of the sward. In the third experiment, diurnal temperature-driven changes in grass concentrations were measured. The results indicate that the grass is behaving as a two-compartment system: (1) a fast-exchanging surface adsorption site with a response time of hours and a capacity essentially independent of K(OA), the octanol-air partition coefficient and (2) a slow responding site with a response time of weeks, the capacity of which is related to K(OA). The kinetic and equilibrium phenomena involved in grass-air exchange are thus complex and are not adequately described by simple first-order rate constants and equilibrium partitioning coefficients.

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)0.000
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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

Citations42
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Science & TechnologySame topicToxic Organic Pollutants ImpactFrench-language works237,207