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Record W2002069647 · doi:10.1021/es026151e

Identification and Characterization of Sorption Domains in Soil Organic Matter Using Structurally Modified Humic Acids

2003· article· en· W2002069647 on OpenAlexaff
Amrith S. Gunasekara, Myrna J. Simpson, Baoshan Xing

Bibliographic record

VenueEnvironmental Science & Technology · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersDepartment of Materials Science and Metallurgy, University of Cambridge
KeywordsSorptionPhenanthreneChemistryHydrolysisPyreneCompostOrganic chemistryOrganic matterEnvironmental chemistryChromatographyAdsorption

Abstract

fetched live from OpenAlex

The sorption of phenanthrene was examined in humic acids (HAs) from different sources: a compost, a peat soil, and a mineral soil. Sub-samples of each HA were subjected to bleaching or hydrolysis to remove predetermined chemical groups from their structures. Bleaching successfully removed a large percentage of rigid, aromatic moieties, whereas hydrolysis removed the mobile, carbohydrate components. Phenanthrene sorption by all HAs was nonlinear (N < 1). However, the phenanthrene isotherms of the bleached HAs were more linear than those of the untreated HAs, whereas the removal of the carbohydrate components by hydrolysis produced more nonlinear isotherms. The introduction of pyrene to the phenanthrene sorption system yielded more linear isotherms for all the HAs, indicative of competitive sorption. Proton spin-spin (1H T2) relaxation determined by nuclear magnetic resonance (NMR) was used to identify separate rigid (condensed) and flexible (expanded) 1H populations and to determine their distribution. These 1H domains were highly sensitive to temperature and correlated well with reported glass transition temperatures for HAs. In combination with the chemical treatments, sorption, and spectroscopic data, we were able to observe some significant relationships among chemical groups, sorption behavior, and structural characteristics.

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 score0.341
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
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 head0.008
GPT teacher head0.210
Teacher spread0.202 · 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

Citations139
Published2003
Admission routes1
Has abstractyes

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