MétaCan
Menu
Back to cohort
Record W2804647541 · doi:10.5772/intechopen.72621

Antioxidants Classification and Applications in Lubricants

2018· book-chapter· en· W2804647541 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

VenueInTech eBooks · 2018
Typebook-chapter
Languageen
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsOxidizing agentChemistryPeroxideChelationBase oilAntioxidantOrganic chemistryBase (topology)Combinatorial chemistryMaterials science

Abstract

fetched live from OpenAlex

Oxidation is a chemical reaction that occurs in lubricants upon exposure to an oxidizing agent such as oxygen and can be catalyzed by copper and iron. Antioxidants are a group of chemicals that can be used in the formulation of lubricants to stop or reduce the rate of oxidation. Based on the mechanism of action, antioxidants are categorized as primary antioxidants (radical scavengers), secondary antioxidants (Peroxide decomposers), and metal deactivators (complex-forming or chelating agents). Selection of the antioxidants in a formulation is a critical decision that depends on the base oil, application and other ingredients in the formulations. Presence of some other ingredients in the product with antagonistic behavior may suppress the role of antioxidants; however, optimal application of antioxidants with synergistic behavior would increase the stabilization impact of the ingredients on the base oil.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.876
Threshold uncertainty score0.957

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)0.0000.000

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 head0.020
GPT teacher head0.231
Teacher spread0.211 · 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