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Record W4382246839 · doi:10.1080/15567249.2023.2229320

Identifying optimal geographic locations for hybrid concentrated solar biomass (HCSB) power plants in Alberta and Ontario, Canada

2023· article· en· W4382246839 on OpenAlex
Mehran Bozorgi, Animesh Dutta, Shohel Mahmud, Syeda Humaira Tasnim

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEnergy Sources Part B Economics Planning and Policy · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicPhotovoltaic Systems and Sustainability
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsBiomass (ecology)Environmental sciencePower stationElectricityConcentrated solar powerEnvironmental engineeringRaw materialElectricity generationSolar powerSolar energyHybrid powerAgricultural engineeringWaste managementPower (physics)EngineeringAgronomyEcologyBiology

Abstract

fetched live from OpenAlex

Solar thermal and biomass hybridization combine two energy sources that complement one another seasonally and diurnally, overcoming their respective disadvantages. In the current research, the feasibility of locating a new medium capacity (5–150 Mwe) hybrid concentrated solar biomass power plant is investigated in Alberta and Ontario. To address the above-mentioned goal, three critical criteria are considered to find the most suitable region. The amount of direct normal irradiation and the biomass feedstock in 50 km and 100 km radii are analyzed in different regions in Ontario and Alberta. The third factor is the distance to the substations. According to the results, although it is difficult to justify developing a hybrid concentrated solar biomass power plant in Ontario, at least five locations in Alberta meet the aforementioned criteria. By concentrating on substations located near natural gas power plants, Calgary, with four natural gas power plants, can be regarded as the most suitable region. According to the findings, we identified that by establishing a 100 Mwe hybrid concentrated solar biomass power plant that requires 359,478 tons of forestry biomass and costs between 3.7 and 4.9 mCAD/Mwe, about 5% of Calgary’s total electricity consumption would be met while reducing CO2 emissions by 32 tons.

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 score0.432
Threshold uncertainty score0.619

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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