Bibliographic record
Abstract
# GEM-MACH for CFFEPS # Source code from Air Quality Research Division, Environment and Climate Change Canada (ECCC), Government of Canada This is a modified version of the chemistry module code from version 2.1.0 of the ECCC GEM-MACH air quality model. GEM-MACH is the model used in the ECCC operational air quality forecast system (e.g., Pavlovic et al., 2016) and this version has been modified to accept fire emissions from the [Canadian Forest Fire Emission Prediction System - CFFEPS](https://github.com/jackenvcan/cffeps). The code posted here is the version of the GEM-MACH source code that was used in the study described in the manuscript entitled "The FireWork air quality forecast system with biomass burning emissions from the Canadian Forest Fire Emissions Prediction System". GEM-MACH is an extension of ECCC's standard GEM numerical weather prediction model, a version of which is available from a [Github repository](https://github.com/mfvalin/gem). The executable for GEM-MACH is obtained by providing this chemistry library to GEM when generating its executable. The GEM-MACH atmospheric chemistry module for the GEM (meteorology) numerical weather prediction model (Copyright ©2007–2013. The GEM-MACH codes are released as free software that can be redistributed and/or modified under the terms of the GNU Lesser General Public License, either version 2.1 or any later version, as published by the Free Software Foundation. *Pavlovic, R., J. Chen, K. Anderson, M.D. Moran, P.-A. Beaulieu, D. Davignon, and S. Cousineau, 2016. The FireWork air quality forecast system with near-real-time biomass burning emissions: Recent developments and evaluation of performance for the 2015 North American wildfire season. J. Air & Waste Manage. Assoc., 66, 819-841, doi:10.1080/10962247.2016.1158214.* --- Internal revision tracking: this is cloned from AQMAS/gem-mach repository branch *m3848_CFFEPS* from revision *AQMAS/gem-mach/commit/7eefc266c90739249ebad15a5d23bb1828ea449f*
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.376 | 0.357 |
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".