Aggregated and disaggregated data about default emission factors in emissions accounting methods from the waste sector
Bibliographic record
Abstract
The dataset presented in this article is related to the research article entitled "Towards improving emissions accounting methods in waste management: A proposed framework" (Maalouf and El-Fadel, 2019) [1] that examines the variability in aggregated and disaggregated emissions from waste management when using commonly adopted international methods (the UN IPCC 2006 Guidelines, the US EPA WARM, the EU EpE protocols, the Canadian IWM, and the UK IWM-2). The dataset presents the aggregated and disaggregated emission factors (EFs) used in existing accounting methods to estimate emissions from the waste sector. The EFs were retrieved from accounting methods to clarify their contribution to variability in estimating emissions across methods. The data contains three parts: aggregated EFs per tonne of waste category for individual waste management processes; disaggregated EFs per management process for a tonne of waste type; and emission flow diagrams of waste management systems for tested methods.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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 teacher head, 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".