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Record W7097504134

REVIEW

2016· article· en· W7097504134 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicConstruction Engineering and Safety
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasingProcurementGovernment (linguistics)VendorChristian ministryPaymentValue for moneySafety standards
DOInot available

Abstract

fetched live from OpenAlex

The District School Board of Niagara appreciates the efforts of parent/community groups who raise money for special projects such as playspace equipment at schools. Provincial grants do not provide funding for playspace equipment since the Ministry of Education does not include the equipment and related activities as part of the curriculum. With the size and complexity of play structures, parent/community groups must provide funds to cover not only the initial costs, but also the cost of ongoing repair and maintenance. Safety considerations are paramount when purchasing playspace equipment. Therefore, parent/community groups are expected to act in accordance with the Administrative Procedures of the Board, and in consultation with Facility Services, regarding the purchase, installation and maintenance of playspace equipment, in an attempt to ensure that: • compliance with the Board Purchasing Policy and the Government of Ontario Broader Public Sector Procurement Directives; • the installation is conducted by a reputable vendor with known safety practices; • new playspace equipment meets the current C.S.A. standards; • the ground surfacing is appropriate and meets current C.S.A. standards; • the best value is derived for the community fund raising efforts; • the playspace equipment is appropriately located and correctly installed;

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation 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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.853
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.027
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0040.004
Open science0.0030.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1470.066

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 head0.005
GPT teacher head0.173
Teacher spread0.169 · 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 source (direct Gemma or distilled Codex), not a consensus.

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
Published2016
Admission routes1
Has abstractyes

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