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Record W2270719185 · doi:10.1021/acs.jchemed.5b00663

Investigating Freezing Point Depression and Cirrus Cloud Nucleation Mechanisms Using a Differential Scanning Calorimeter

2016· article· en· W2270719185 on OpenAlexfundno aff
Kentaro Y. Bodzewski, Ryan Caylor, Ashley M. Comstock, Austin T. Hadley, Felisha Imholt, Kory D. Kirwan, Kira S. Oyama, Matthew E. Wise

Bibliographic record

VenueJournal of Chemical Education · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsnot available
FundersConcordia University
KeywordsCirrusIce nucleusNucleationAerosolDifferential scanning calorimetryCalorimeter (particle physics)Cloud chamberAmmonium sulfateChemistryHomogeneousIce crystalsMineralogyAtmospheric sciencesMeteorologyThermodynamicsPhysicsOpticsChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

A differential scanning calorimeter was used to study homogeneous nucleation of ice from micron-sized aqueous ammonium sulfate aerosol particles. It is important to understand the conditions at which these particles nucleate ice because of their connection to cirrus cloud formation. Additionally, the concept of freezing point depression, a topic commonly encountered in undergraduate science courses, was experimentally studied using an instrumental technique. As expected, when the concentration of ammonium sulfate in the particles increased, their freezing and melting temperatures decreased. The data was consistent with previous work that showed water activity is the determinant for homogeneous ice nucleation in aerosol particles. This laboratory experiment is appropriate for students enrolled in upper division atmospheric science, chemistry and instrumental analysis courses.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

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 integrity0.0000.001
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 head0.020
GPT teacher head0.262
Teacher spread0.242 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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