From coal to wood thermoelectric energy production: a review and discussion of potential socio-economic impacts with implications for Northwestern Ontario, Canada
Bibliographic record
Abstract
The province of Ontario in Canada is the first North American jurisdiction withlegislation in place to eliminate coal-fired thermoelectric production by theend of 2014. Ontario Power Generation (OPG) operates coal-fired stations inOntario, with Atikokan Generating Station being the only facility slated toswitch to 100% woody biomass. It is anticipated that this coal phase out policywill have socio-economic impacts. Because of these anticipated changes, in thispaper, we review the current state of peer-reviewed literature relating to threeburning scenarios (biomass, coal and co-firing) in order to explore theknowledge gaps with regard to socio-economic impacts and identify research needswhich should elucidate the anticipated changes on a community level. We reviewedover 150 sources, which included peer-reviewed articles and non-peer-reviewedgrey literature such as government documents, non-governmental organizationreports and news publications. We found very few peer-reviewed articles relatedto Canadian studies (even fewer for Ontario) which look at woody biomass burningfor thermoelectric production. We identify a number of socio-economic impactassessment tools readily available and present potential criteria required inselecting an appropriate tool for the Ontario context. For any tool to providemeaningful results, we propose that appropriate and robust local data must becollected and analyzed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".