Development of a risk assessment software for cumulative effect
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 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 it