MétaCan
Menu
Back to cohort
Record W2109182365 · doi:10.1139/v10-174

Computer study of absorption of oxygen and ozone molecules by water clusters with Cl<sup>–</sup> and Br<sup>–</sup>

2011· article· en· W2109182365 on OpenAlexvenueno aff
А. Е. Галашев

Bibliographic record

VenueCanadian Journal of Chemistry · 2011
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryOzoneOxygenRaman spectroscopyAbsorption (acoustics)BromineMoleculeIonChlorineInfraredAbsorption spectroscopyPhotochemistryAnalytical Chemistry (journal)Infrared spectroscopyEnvironmental chemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Infrared absorption and Raman spectra were calculated by using the molecular dynamics method for water clusters with chlorine and bromine ions in a medium of water and either ozone or oxygen molecules. The intensity of IR absorption spectra of clusters with absorbed oxygen increased and that of clusters with absorbed ozone decreased as the number of chlorine ions grew. When Br– were present in the system the inverse behaviour was observed. An increase in the number of ions weakened the intensity of the Raman spectra when either oxygen or ozone was absorbed; for ozone this weakening was more noticeable. A stronger reduction of the integrated intensity of the Raman spectrum with an increase in the number of Br– was observed in the presence of ozone molecules in the system. Cl– ions caused an amplification of the emission power of the IR radiation for both systems in the presence of oxygen and ozone, and Br– strengthened the emission of IR radiation for systems containing ozone, and weakened it for systems with oxygen molecules.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0030.000

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 head0.191
Teacher spread0.183 · 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 designSimulation or modeling
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

Citations8
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of ChemistrySame topicSpectroscopy and Quantum Chemical StudiesFrench-language works237,207