MétaCan
Menu
Back to cohort
Record W3194138279 · doi:10.1039/d1em00248a

Ionization selectivity of electrospray and atmospheric pressure photoionization FT-ICR MS for petroleum refinery wastewater dissolved organic matter

2021· article· en· W3194138279 on OpenAlexaff
Chen He, Zhi Fang, Yongyong Li, Chunqing Jiang, Suoqi Zhao, Chunming Xu, Yahe Zhang, Quan Shi

Bibliographic record

VenueEnvironmental Science Processes & Impacts · 2021
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsChemistryWastewaterFourier transform ion cyclotron resonanceDissolved organic carbonElectrospray ionizationMass spectrometryPhotoionizationOil refineryEnvironmental chemistryIonizationAnalytical Chemistry (journal)ChromatographyIonOrganic chemistryEnvironmental engineeringEnvironmental science

Abstract

fetched live from OpenAlex

= 0-2) classes were dominant in the wastewater DOM detected in negative-ion and positive-ion ESI mode, respectively. And the wastewater DOM contains considerable amounts of polycyclic aromatic hydrocarbons that did not respond to ESI but can be ionized selectively by APPI. Compared with riverine DOM, the refinery wastewater DOM has a higher molecular complexity and is more enriched in hydrocarbon, and nitrogen- and sulfur-containing compounds. The results show that the major components of refinery wastewater DOM were distinctive from those of the natural organic matter. Though not quantitative, the results obtained by various ionization techniques were found to be complementary, and are helpful to our understanding of the selectivity of different ionization techniques as well as the molecular compositions of DOM.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.004
GPT teacher head0.213
Teacher spread0.209 · 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 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

Citations35
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Science Processes & ImpactsSame topicMass Spectrometry Techniques and ApplicationsFrench-language works237,207