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Record W4229960860 · doi:10.1016/s1365-6937(09)70161-3

H2O Innovation Inc, Canada

2009· article· en· W4229960860 on OpenAlexaboutno aff

Bibliographic record

VenueFiltration Industry Analyst · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsnot available
Fundersnot available
KeywordsEarnings before interest, taxes, depreciation, and amortizationFinanceAmortizationFixed assetEquity (law)TariffBusinessProduction (economics)DebtEconomicsContext (archaeology)Diversification (marketing strategy)International economicsMacroeconomics

Abstract

fetched live from OpenAlex

Access to electricity is a major issue in West Africa. Governments have a difficult equation to solve. They naturally seek to offer their people a cheap kWh. But they are constrained by a production based largely on oil and therefore highly volatile production costs. How to fix an acceptable tariff, taking into account the investment needs required to expand the network and increase production? This analysis should provide some answers.The study presented in this paper provides a financial analysis of electricity utilities in West Africa. It allows a comparison of performances on a number of key financial ratios related to operations (Earning Before Interest Taxes Debt and Amortization/sales, working capital requirement/sales, days of receivables or payables), investment (net fixed assets/gross fixed assets), bank financing (financial structure, debt/EBITDA, interest expense/EBITDA) and economic and financial returns (Return On Capital Employed, Return On Equity).The conclusion focuses on the growth opportunity that the electricity sector could represent for each country. But this opportunity may only materialize if the EBITDA margins are restored. The available options appear limited and must be assessed taking into account the context of each country: tariff increase, improvement of technical losses or diversification into means of production no longer based primarily on oil or gas.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.335
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.227
Teacher spread0.207 · 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
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

Citations0
Published2009
Admission routes1
Has abstractyes

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