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Record W4313210683 · doi:10.3390/app122412753

Tracking the Biogenic Component of Lower-Carbon Intensive, Co-Processed Fuels—An Overview of Existing Approaches

2022· article· en· W4313210683 on OpenAlexaff
Adrian O’Connell, Jianping Su, Anna Ringsred, Matteo Prussi, Jack Saddler, Nicolae Scarlat

Bibliographic record

VenueApplied Sciences · 2022
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRadiocarbon datingFossil fuelEnvironmental scienceCarbon fibersProcess engineeringYield (engineering)Component (thermodynamics)Accelerator mass spectrometryWaste managementMaterials scienceEngineeringGeologyMetallurgy

Abstract

fetched live from OpenAlex

Several methods are currently used to track the bio-component of co-processed fuels including energy/mass balance, yield methods and radiocarbon techniques. The methods used to track or estimate the bio-component of fuels produced when bio and fossil feedstocks are processed together (co-processed) in oil refineries were analysed in detail, together with their advantages and disadvantages. Some methods, such as radiocarbon methods that allow the direct measurement of the bio-content in a fuel, have been criticised due to low accuracy at low blends. However, these reservations have tended to misinterpret the options available for carbon dating and to discount recent improvements in these tests. As much higher co-pressing mixtures are anticipated if published national decarbonisation targets are to be achieved, any challenges at very low co-processing ratios affecting the accuracy of the radiocarbon methods should not be an issue. Energy/mass balance and yield methods might be supplemented with carbon-tracking to determine the real the biogenic content.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0070.005
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.255
GPT teacher head0.361
Teacher spread0.106 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2022
Admission routes1
Has abstractyes

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