Composting - Households and the Environment Survey (2007, 2009, 2011, and 2013) [Excel]
Bibliographic record
Abstract
The Households and the Environment Survey (HES) was conducted from October 2007 to February 2008, October and November 2009, 2011, and 2013 as a supplement to the Canadian Community Health Survey. The survey was designed to specifically address the needs of its funding source the Canadian Environmental Sustainability Indicators (CESI) project, a joint venture between Statistics Canada, Environment Canada and Health Canada. The CESI project reports annually on air quality, water quality and greenhouse gas (GHG) emissions in Canada using indicators to identify areas of importance to Canadians and monitor progress. The HES was first conducted in 1991, 1994 and more recently in 2006. The most recent four surveys (2007, 2009, 2011 and 2013) offer an expanded view on household behaviours that relate to the environment but allows for comparisons with the 1994 survey for some indicators and most of the indicators from the 2006 survey. This dataset is specifically for composting at provincial and CMA levels. It shows the trend of people's awareness of composting across Canada. Composting helps reduce the amount of waste to landfill and the amount of greenhouse gas emissions in the landfills.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.011 |
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".