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Record W4233097736 · doi:10.1017/9781108235594.027

A CBA of the North-East Mine Development Project

2018· book-chapter· en· W4233097736 on OpenAlexaff
David H. Greenberg, Aidan R. Vining, David L. Weimer

Bibliographic record

VenueCambridge University Press eBooks · 2018
Typebook-chapter
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsDiscountingValuation (finance)PreferenceCraftContingent valuationTime preferenceVariety (cybernetics)Cover (algebra)Cost–benefit analysisManagement scienceComputer scienceOperations researchEconomicsActuarial scienceRisk analysis (engineering)EngineeringMicroeconomicsBusinessWillingness to payPolitical scienceArtificial intelligenceFinanceGeography

Abstract

fetched live from OpenAlex

Cost-Benefit Analysis provides accessible, comprehensive, authoritative, and practical treatments of the protocols for assessing the relative efficiency of public policies. Its review of essential concepts from microeconomics, and its sophisticated treatment of important topics with minimal use of mathematics helps students from a variety of backgrounds build solid conceptual foundations. It provides thorough treatments of time discounting, dealing with contingent uncertainty using expected surpluses and option prices, taking account of parameter uncertainties using Monte Carlo simulation and other types of sensitivity analyses, revealed preference approaches, stated preference methods including contingent valuation, and other related methods. Updated to cover contemporary research, this edition is considerably reorganized to aid in student and practitioner understanding, and includes eight new cases to demonstrate the actual practice of cost-benefit analysis. Widely cited, it is recognized as an authoritative source on cost-benefit analysis. Illustrations, exhibits, chapter exercises, and case studies help students master concepts and develop craft skills.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.296
Threshold uncertainty score0.953

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 head0.026
GPT teacher head0.172
Teacher spread0.146 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2018
Admission routes1
Has abstractyes

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