Bibliographic record
Abstract
Abstract Unmanned aerial systems (UAS) are lightweight, low-cost aircraft platforms operated from the ground which can be outfitted with imaging or non-imaging payloads (‘drones’). UAS offer Health Environment and Safety (HES) professionals a promising opportunity to reduce health, environmental and safety risks by keeping people out of harm's way, reducing exposure to potential health hazards, and for performing non-invasive surveys of ecological features. A certified operator and unit were retained in the field full-time to support a greenfield gas development in a rugged, remote area. Ready access to a UAS provided timely data to inform field decision making. "If in doubt, put the drone up" became a common phrase in the field, affirming the value of UAS imagery as an information-providing and risk-mitigating tool during site development. UAS collected the data and information that would have otherwise put people on aerial work platforms, in helicopters or on the ground in remote, rugged locations, avoiding thousands of safety-critical workforce hours. UAS were employed to perform reconnaissance, monitoring and data collection for a wide range of HES applications: Safety: reconnaissance of potential high-consequence situations (landslides, road washouts, avalanche assessment) and access to difficult locations (stack and powerline inspections, landfill slope stability assessment)Environmental: non-invasive environmental monitoring of wildlife (raptor nests, large mammals) and environmental features (marine eelgrass, forest)Health: hazardous materials (hazmat) surveys of legacy facilities to support decommissioning (asbestos-containing roofing materials) In addition to enabling information-based decision-making in the field by providing real-time imagery, UAS visually conveyed information that was useful for communicating with stakeholders and regulators. This paper will demonstrate that onsite UAS can provide timely, cost-effective information and reduce HES risks in the field by replacing the human element for some safety-critical tasks.
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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 | 0.000 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".