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Record W3100132304

Magic Numbers for Classical Lennard-Jones Cluster Heat Capacities

2008· article· en· W3100132304 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicnanoparticles nucleation surface interactions
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsHeat capacityCluster (spacecraft)Magic number (chemistry)ThermodynamicsMonte Carlo methodCluster sizeMonotonic functionSpecific heatChemistryStatistical physicsPhysicsMAGIC (telescope)Materials scienceMolecular dynamicsComputational chemistryMathematics
DOInot available

Abstract

fetched live from OpenAlex

Heat capacity curves as functions of temperature for classical atomic clusters bound by pairwise Lennard-Jones potentials were calculated for aggregate sizes 4 ≤ N ≤ 24 using Monte Carlo methods. J-walking (or jump-walking) was used to overcome convergence difficulties due to quasi-ergodicity in the solid-liquid transition region. The heat capacity curves were found to differ markedly and nonmonotonically as functions of cluster size. Curves for N = 4, 5 and 8 consisted of a smooth, featureless, monotonic increase throughout the transition region, while curves for N = 7 and 15–17 showed a distinct shoulder in this region; the remaining clusters had distinguishable transition heat capacity peaks. The size and location of these peaks exhibited “magic number ” behavior, with the most pronounced peaks occurring for magic number sizes of N = 13, 19 and 23. This is consistent with the magic numbers found for many other cluster properties, but there are interesting differences for some of the other cluster sizes. Further insight into the transition region

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.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.039
GPT teacher head0.237
Teacher spread0.198 · 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