Recent Health and Safety Incident Trends Related to the Storage of Woody Biomass: A Need for Improved Monitoring Strategies
Bibliographic record
Abstract
Self-heating fires, dust explosions and off-gassing during biomass storage are serious hazards which can have devastating consequences, resulting in worker fatalities and health impacts, as well as bioenergy plant destruction and complete loss of production. A compilation of incident reports involving biomass storage from 2000–2018 has revealed that these potential hazards continue to be a major concern in the bioenergy sector. Higher occurrence rates were found for incidents categorized as self-heating fires and fires of uncertain causes in recent years through our study of online reports. This paper highlights a critical need for improved safety protocols for bioenergy plant workers, detailed incident documentation and enhanced biomass monitoring strategies to drastically reduce the occurrence of threats associated with the storage of woody biomass. In order to manage the high risks associated with self-heating, a system for real-time monitoring of internal pile temperature was investigated. A monitoring system supplied by Braingrid Corporation was verified using embedded Tinytag thermologgers indicating that this methodology shows potential for preventing spontaneous combustion events by providing real time temperature data for superior pile management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".