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Record W2771033558 · doi:10.1080/00207233.2017.1389567

The carbon footprint and environmental impact assessment of desalination

2017· article· en· W2771033558 on OpenAlex
Fahad Ameen, Jacqueline Stagner, David S.‐K. Ting

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Environmental Studies · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicWater-Energy-Food Nexus Studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsDesalinationGreenhouse gasCarbon footprintEnvironmental impact assessmentEnvironmental scienceEnvironmental engineeringLife-cycle assessmentEnvironmental protectionPollutionEnvironmental pollutionFossil fuelWaste managementEngineeringProduction (economics)Ecology

Abstract

fetched live from OpenAlex

Desalination is an important means to meet water needs in many countries. The existing process is costly and energy intensive and further strains the environment with brine disposal and greenhouse gas (GHG) emissions. This paper describes several factors that are to be considered in desalination plants, such as the use of the land, the contamination of groundwater and the marine environment, the use of energy, and noise pollution. One major indirect environmental impact is the production of the energy required to run the desalination plants, particularly from burning oil, which increases GHG emissions. The carbon footprints associated with sea water desalination plants in the United Arab Emirates are assessed along with the other factors affecting human and marine life. There is no standard environmental impact assessment method, but the World Health Organization has begun work to produce one.

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.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.493

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.018
GPT teacher head0.319
Teacher spread0.301 · 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