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Record W2074496579 · doi:10.1021/jp0312722

“Icebergs” or No “Icebergs” in Aqueous Alcohols?:  Composition-Dependent Mixing Schemes

2004· article· en· W2074496579 on OpenAlexaff
Yoshikata Koga, Keiko Nishikawa, Peter Westh

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2004
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIcebergChemistryPercolation (cognitive psychology)Mixing (physics)Hydrogen bondRange (aeronautics)Chemical physicsMoleculePercolation thresholdAqueous solutionThermodynamicsStatistical physicsPhysical chemistryPhysicsMaterials scienceSea iceOrganic chemistryMeteorology

Abstract

fetched live from OpenAlex

The classical concept of “iceberg formation” is modified by our recent thermodynamic studies. The local enhancement of the hydrogen-bond network of H 2 O in the immediate vicinity of small nonelectrolyte solutes (i.e., the “iceberg formation”) is still correct. However, the hydrogen-bond probability of bulk H 2 O away from solutes is reduced progressively, as the solute composition increases. When the hydrogen-bond probability of bulk H 2 O is reduced to the bond percolation threshold of the hexagonal ice connectivity, the hydrogen-bond percolation is lost and a qualitatively different mixing scheme sets in, whereby the solution consists of two kinds of clusters. In the solute-rich region, solute molecules form clusters of its own kind. Thus, the “iceberg formation” is basically correct within a narrow range in the H 2 O-rich region for small nonelectrolyte solutes. Thus, reference made to the “iceberg” concept in recent literatures should be clarified in terms of the concentration range and the size of solute in question.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.007
GPT teacher head0.228
Teacher spread0.221 · 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

Citations76
Published2004
Admission routes1
Has abstractyes

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