MétaCan
Menu
Back to cohort
Record W2032871257 · doi:10.4018/jsesd.2011100102

When Low-Carbon means Low-Cost

2011· article· en· W2032871257 on OpenAlexfundaboutno aff
Stephen J. Salter

Bibliographic record

VenueInternational Journal of Social Ecology and Sustainable Development · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
FundersNatural Resources CanadaWorld Resources Institute
KeywordsGreenhouse gasIndustrial ecologyFossil fuelNatural resource economicsRevenueWaste managementEnvironmental scienceSustainabilityBusinessEcologyEconomicsEngineering

Abstract

fetched live from OpenAlex

Ecology is often discussed as a matter of balance, in which environmental protection must be affordable and not interfere with jobs or the economy. At the same time, the economy is based on wastefulness. It has been estimated that the embodied energy in wasted food in the United States is greater than the energy available from the production of ethanol and from the annual yield from petroleum drilling in the outer continental shelf (Cuéllar & Webber, 2010). In addition, rising demand for fossil fuels is being met by sources that bring increasing environmental risk. This paper summarizes the industrial ecology aspects of a 2010 study completed by a cross-functional team of specialists in ecology, engineering, economics, and governance in Vancouver, Canada. The Integrated Resource Recovery Study, Metro Vancouver North Shore Communities (the North Shore Study) modeled the value of producing reclaimed water, electricity, and heat from wastewater, clean organic wood waste, and waste heat from industry simultaneously. The results suggest that this integrated approach could yield significant ecological benefits, and reduce the community’s greenhouse gas emissions by 25%. In addition, revenues from sales of recovered heat, water, greenhouse gas credits, and fertilizer could significantly reduce the cost of municipal waste management to taxpayers.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.152
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.000

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 head0.007
GPT teacher head0.211
Teacher spread0.205 · 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
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Social Ecology and Sustainable DevelopmentSame topicAgriculture Sustainability and Environmental ImpactFrench-language works237,207