From Fibre Basket to Fibre Boiler: Climate Change, Bioenergy, and Making Waste in the Political Forest
Bibliographic record
Abstract
Abstract In British Columbia (BC), Canada, industry advocates are increasingly mobilising bioenergy as a renewable and carbon neutral fuel source in energy transition and climate change mitigation strategies. At the same time, changing climate conditions have exacerbated the extent and severity of beetle and fire activity in the region, and trees impacted by extreme climate events are allocated as salvage wood in a growing bioenergy sector. This paper draws from timber supply and sector growth data in the Cariboo–Omineca fibre “basket” and follows the global wood pellet supply chain that links trees from BC to heat and power generation in the UK, the fibre “boiler”. The basket and boiler metaphors expose the powerful combination of state policy and capital accumulation in both BC and European contexts, and the discourses of waste and salvage that enabled sectoral growth in wood pellet manufacturing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".