A Risk‐Aware Design and Control Scheme for Canadian Salmon Wildlife Monitoring Systems
Bibliographic record
Abstract
ABSTRACT Accurate wildlife monitoring is essential for environmental protection and wildlife conservation. Current wildlife monitoring systems (WMS) focus on the communication network construction while neglecting the necessity of maintaining a stable power supply to the WMS. The optimal power control of the WMS elements lacks a comprehensive optimization method and an appropriate approach to deal with uncertainties. Therefore, this work proposes a risk‐aware design and control scheme for the WMS microgrid to reduce WMS investment costs with a detailed WMS model considering both power balance and data flow constraints. In particular, we utilize time‐shifting characteristics from data transmission of the satellite dish and introduce the deep discharging scheme of batteries to provide flexibility for resilient WMS operations. A power outage risk index based on WMS state estimation is designed for the evaluation of the real‐time power supply capability. We perform simulations on the practical Pacific wild salmon WMS in Canada to validate the proposed scheme. The risk‐aware WMS design and control scheme greatly reduce WMS investment costs and enhance WMS power supply. The battery deep discharging scheme and the proposed risk index decrease the load shedding and avoid power outages of monitoring devices in the practical Pacific wild salmon WMS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".