Low-Carbon Building Skills Website
Bibliographic record
Abstract
Low-carbon building involves designing, constructing, operating, maintaining, and removing buildings in ways that conserve natural resources and reduce Greenhouse Gas (GHG) emissions. To move towards a low-carbon economy, we need tradespeople who are educated in the design, construction maintenance and operation of buildings, who understand the industrial and constructions sectors, and are trained in low-carbon building skills.\nSheridan College’s participation in the Low Carbon Building Skills (LCBS) project involved developing and delivering low-carbon building skills curriculum across relevant disciplines and involving the full building cycle, from design to operations and optimization. The learning modules address what can be done to reduce and/or eliminate the use of carbon in new and existing buildings from a variety of disciplines.\nDesigned for professors of Ontario Post Secondary institutions, access to course material is granted with verification of a post-secondary email address. Through instruction of the LCBS modules, students will gain experience in design, implementation, operation, optimization and troubleshooting of building systems which will lead to building with a net decrease in energy consumption and GHG production resulting in reduced carbon emissions within Ontario.\nAccess note:\nhttps://lowcarbonbuilding.sheridancollege.ca/copyright-and-terms-of-use/\nFor access inquiries, please contact fast_events@sheridancollege.ca\nBrowser requirements: Chrome or Firefox. Internet Explorer is not supported.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.697 | 0.423 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".