MétaCan
Menu
Back to cohort
Record W1988377177 · doi:10.2202/1542-6580.1905

Bioenergy II: Group 8 Metal Complexes as Homogeneous Ionic Hydrogenation and Hydrogenolysis Catalysts for the Deoxygenation of Biomass to Petrochemicals - Opportunities, Challenges, Strategies and the Story so Far

2009· article· en· W1988377177 on OpenAlexaff
Marcel Schlaf, M.E. Thibault, Domenico DiMondo, Deeb Taher, Elham Karimi, Devipriya Ashok

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2009
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsDeoxygenationHydrogenolysisCatalysisPetrochemicalBiomass (ecology)ChemistryMetalRutheniumOrganic chemistryInorganic chemistryChemical engineeringAgronomy

Abstract

fetched live from OpenAlex

Biomass is characterized by a high oxygen content (typically ? 40 % w/w). Its conversion into feedstocks that can directly be used in existing petrochemical feed streams will therefore require an efficient and selective deoxygenation chemistry. Ruthenium or iron-based water/acid-tolerant and high-temperature stable metal complexes that are active ionic hydrogenation and hydrogenolysis catalysts maybe ideally suited for this purpose.

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 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)0.001
metaresearch head score (Gemma)0.001
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 score0.019
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.021
GPT teacher head0.249
Teacher spread0.228 · 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 teacher head, 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

Citations1
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Chemical Reactor EngineeringSame topicCatalysts for Methane ReformingFrench-language works237,207