MétaCan
Menu
Back to cohort
Record W589839138

Consumptive Water Use in Bioethanol and Petroleum Gasoline Pathways

2010· article· en· W589839138 on OpenAlexaboutno aff
May Wu, Marianne Mintz, Michael Wang, Salil Arora

Bibliographic record

VenueTransportation Research Board 89th Annual MeetingTransportation Research Board · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGasolineBiofuelEnvironmental sciencePetroleumWater useCellulosic ethanolWaste managementPetroleum productFossil fuelEngineeringChemistryCelluloseAgronomy
DOInot available

Abstract

fetched live from OpenAlex

Energy production requires substantial water input. Biofuel feedstocks like corn, switchgrass, and agricultural residues need water for growth and conversion to bioethanol. Likewise, petroleum feedstocks like crude oil and oil sands require large volumes of water for drilling, extraction and conversion into refined products. Water management has become a key feature of existing projects and a potential issue in new ones. This paper examines the growing issue of water use in energy production by characterizing current consumptive water use in liquid fuel production. “Consumptive water use” is defined as the sum total of process water input less water output that is recycled and reused for the process. The estimate applies to surface and groundwater sources but does not include precipitation. Water requirements are evaluated for five fuel pathways: bioethanol from corn, bioethanol from cellulosic feedstocks, gasoline from Canadian oil sands, gasoline from Saudi Arabian crude oil, and gasoline from conventional crude oil produced from U.S. onshore wells. Regional variations and historic trends are noted, as are opportunities to reduce water use.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.328
Teacher spread0.266 · 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.

Study designObservational
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
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueTransportation Research Board 89th Annual MeetingTransportation Research BoardSame topicWater-Energy-Food Nexus StudiesFrench-language works237,207