Predictive Analytics for Supporting Environmental Sustainability and Disaster Management
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
As we are living in an uncertain world, the uncertainty may have significant and/or direct implications on various aspects of the computer industry. Hence, innovations of computers, software and applications have emerged as a pressing need. Data science solutions have been designed and/or developed for social good. For example, in this paper, we present a predictive analytics solution for supporting environmental sustainability. In particular, we focus on environmental sustainability and disaster management related to water levels. To elaborate, low water levels may lead to unsustainability in water supply. In contrast, high water levels may lead to hazards or disasters like floods. Thus, having a reliable predictive analytics solution that gives accurate estimates of water levels is important. Our solution integrates different categories of weather data collected from distributed rich data sources. Evaluation on real-life data from a Canadian city demonstrates the practicality of our solution in predicting water level, and thus supports environmental sustainability and disaster management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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