Intelligent Analysis of the Ecological State of Environment with Application of Distributed Expertise (on the Example of Bryansk Region)
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
The paper considers the problem of assessing the ecological state of the environment in the region. An approach to the intelligent analysis and estimation of anthropo-technogenic pollution of a territory with the application of integral indicators which take into account environmental pollution is proposed. To estimate the integral indicator parameters, distributed group expertise technology is used, supporting a mechanism for control of expert estimates consistency, taking into account experts’ competency in the relevant subject areas. Using the proposed approach, the problem of risk assessment of environmental impact of chemical air pollutants has been solved. Methods for control of expert estimates consistency based on the procedure of feedback with experts made it possible to increase the reliability of evaluation results and also to decrease the influence of a random expert error on the final assessment. The obtained aggregated risk estimates were used to construct, calculate and visualize the integral indicator of radioactive and chemical contamination of the districts of Bryansk region.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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