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Record W2031372163 · doi:10.1021/es702523w

Elemental, Isotopic, and Spectroscopic Assessment of Chemical Fractionation of Dissolved Organic Matter Sampled with a Portable Reverse Osmosis System

2008· article· en· W2031372163 on OpenAlexaff
Alexandre Ouellet, Dragosh Catana, Jean‐Baptiste Plouhinec, Marc Lucotte, Yves Gélinas

Bibliographic record

VenueEnvironmental Science & Technology · 2008
Typearticle
Languageen
FieldChemistry
TopicAnalytical chemistry methods development
Canadian institutionsConcordia UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsReverse osmosisFractionationEnvironmental chemistryOrganic matterDissolved organic carbonChemistryEnvironmental scienceChromatographyMembraneOrganic chemistry

Abstract

fetched live from OpenAlex

Portable reverse osmosis (RO) systems are increasingly being used for isolating dissolved organic matter (DOM) from freshwater aquatic systems because of their high volume processing capacity and high absolute DOM recoveries. However, obtaining complete recoveries implies the rinsing of the reverse osmosis system with a solution of dilute NaOH and combining the rinse solution and the DOM concentrate. Because of the potential chemical alterations that can affect the integrity of the organic pool leached from the RO system at high pHs, this approach is not compatible with studies based on the molecular-level analysis of DOM. The potential for elemental, isotopic, and chemical fractionation was thus evaluated on a series of freshwater DOM samples concentrated in the field with a portable RO system when the concentrate and the rinse solution are not combined. DOC recoveries in the concentrate varied between 81.6 and 88.8%, and total balance calculations showed total recoveries of dissolved and particulate organic carbon ranging between 96.4 and 106.9%. Despite similar delta13C signatures, differences in N content and FTIR-based chemical composition between the concentrate and the rinse DOM solutions suggest some degree of chemical fractionation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.277
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.242
Teacher spread0.234 · 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.

Study designBench or experimental
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

Citations14
Published2008
Admission routes1
Has abstractyes

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