MétaCan
Menu
Back to cohort

Progresses on Thermodynamic Databases

2018· article· en· W2884364315 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

VenueIOP Conference Series Materials Science and Engineering · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGeochemistry and Geochronology of Asian Mineral Deposits
Canadian institutionsScience North
FundersNational Natural Science Foundation of China
KeywordsSustainabilityThermodynamicsDatabaseUniversality (dynamical systems)Computer scienceMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Chemical production is accompanied by heat generation and the generation of new substances. The use of these calories and products, for sustainable development and environmental protection is of great significance. Thermodynamics is the understanding and mastery of the nature of heat and the law of energy conversion. It is a macroscopic theory based on experiment and has a high degree of universality and reliability. Thermodynamic data is an essential basic data for chemical design, simulation, and production. Therefore, thermodynamic data is of great significance to the sustainability of chemical production and environmental protection. Several relatively complete thermodynamic database around the world such as FactSage, THERMO-CALC, HSC Chemistry, inorganic thermochemical database, metallurgical thermodynamic database and so on were summarized and discussed.

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 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 score0.173
Threshold uncertainty score0.552

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.022
GPT teacher head0.212
Teacher spread0.190 · 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