Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it