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Land Portfolio Managed Decision Support System For Renewable Energy Investments

2025· article· en· W4413188575 on OpenAlex

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

Venuenot available
Typearticle
Languageen
FieldEngineering
Topic3D Modeling in Geospatial Applications
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsPortfolioRenewable energyDecision support systemEnvironmental economicsBusinessComputer scienceNatural resource economicsFinanceEconomicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

A web-based portfolio management application has been developed that will accelerate the business processes of real estate agents who have difficulty in land portfolio management and marketing in the real estate market, and provides statistical data about the land so that they can produce a marketing strategy, performs solar energy analysis about the land that customers can buy, and makes investment suggestions for renewable energy systems such as PV panels or wind. Thanks to the developed application, land images can be shown to customers as drawings on the web. The aim of the study is to estimate the maximum PV panel power or wind power that the customer can build on the relevant land in the future, and the income that can be obtained from this, depending on the daily, monthly and annual solar radiation or wind energy average, and the investment cost and profit analysis are presented in the form of graphs. Thus, by playing the role of a decision support mechanism, it is possible to direct the customer to the most suitable land according to the investment considered.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.897
Threshold uncertainty score0.385

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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