Development of a risk assessment software for cumulative effect
Bibliographic record
Abstract
Regional Risk Assessment is essential for evaluating the environmental impacts of large-scale resource development projects. However, existing Regional Asessement (RA) frameworks often lack generalizability which hinders result standardization. To address these challenges, the Risk Assessment Framework for Cumulative Effects (RAFCE) was developed to provide a standardized approach for impact identification, prioritization, and mitigation during RA. Despite these strengths, the RAFCE's reliance on spreadsheet-based manual data entry and calculations, coupled with the absence of collaborative features, increases the risks of human error and inflates operational costs including time taken to complete an RA. This paper proposes a software implementation of RAFCE to enhance efficiency and accuracy in the RA process. This is a novel approach that provides a platform unique of its kind for systematically evaluating the cumulative effects of resource exploration by multiple stakeholders. The development process involved three main steps:•Developing a NoSQL Database to efficiently store and retrieves RA data,•Implementing an API and Backend with Java Spring Boot automates critical functionalities and•Building a React-based Frontend Development: that provides a user-friendly interface, that simplifies data entry and software interaction.By automating calculations and improving the user interface, the proposed software mitigates the risks associated with manual processes and significantly reduces the cost and time required for the RA process, thereby enhancing its reliability and efficiency.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".