Replication Data for: Canada's Landfill Methane Inventories: The Challenge of Accurate Modeled and Measurement-Based Emissions
Bibliographic record
Abstract
This dataset provides information about 42 anonymized Canadian landfills. We conducted mobile methane measurements at these landfills in 2022. The dataset includes parameters from independant FOD modeling and the resulting emission rates, the estimated emission rates from the mobile surveys, and information about the climate and province. lfid, anonymized landfill ID province, location of the landfill status, open or closed yr_precip, total annual precipitation (2018-2022) in mm temperature, average temperation (2018-2022) in degC climate_cluster, climate category used in the paper, based on precipitation and temerature waste_accumulated_2022, in tonne waste_accumulated_2021, in tonnes Lo_IPCC_accumulated_2022, potential methane that could be generated, in tonnes methane_generated_2022, in tonnes methane_flared_2022, as reported by operators, in tonnes methane_utilized_2022, as repoerted by operators, in tonnes ECCC_2022_rate, estimated FOD based rate in 2022 ECCC_2021_rate, estimated FOD based rate in 2021, truck_GPM_rate, estimate measurement-based emission rate in 2022 truck_GPM_rate_bias_corrected, bias-corrected measurement-based emission rate in 2022 GHGRP_2022, emission rate in 2022 as reported by the operators
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".