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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 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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.255
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations7
Published2022
Admission routes1
Has abstractyes

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