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Record W7097345455

1 An Energy Audit at Credit Valley Conservation

2015· article· en· W7097345455 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicEnergy Efficiency and Management
Canadian institutionsnot available
Fundersnot available
KeywordsAuditEnergy conservationBlueprintIncentiveOrder (exchange)Energy consumptionConsumption (sociology)Audit plan
DOInot available

Abstract

fetched live from OpenAlex

2 Credit Valley Conservation Authority (CVC) is currently undergoing expansion in programs and staff. Due to this, increased energy consumption will result, which will have an effect upon the environment. This report has been issued in order to remedy that situation by conducting an energy audit of CVC’s operations. Conducting an audit has many organizational implications. First, in order for the audit to be effective, staff should become more aware of the implications their behaviour has, which can be done through seminars and training programs. This will lead staff to having pro-environmental behaviours, which will aid the effectiveness of the audit. In addition, pro-environmental behaviour can be as simple as turning off lights when leaving a room. It is also important to remember that staff must be involved in the energy audit so that energy consumption will drop, which will aid in implementing further projects because of the money saved. The audit itself consists of two main parts. The first part is the collection of records, including energy bills and building blueprints and drawings. The second part is a screening survey, which examines the consumption patterns of major equipment, if energy is leaking through the building and what potential measures might be taken to decrease consumption. If CVC wishes to implement the recommended measures in the audit, there are many financial incentives available to do so. These include the Energy Retrofit Assistance from Natural Resources Canada and other incentives. Lastly, CVC may want to consider larger scale projects such as solar walls or small wind turbines, which are covered by the incentives. 3

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.831
Threshold uncertainty score1.000

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)0.0010.000

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 head0.030
GPT teacher head0.246
Teacher spread0.216 · 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.

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
Published2015
Admission routes1
Has abstractyes

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