The use of modern information technologies in combating crimes against the environment
Bibliographic record
Abstract
The scientific publication is dedicated to researching the possibilities of modern information technologies in combating environmental crime. In particular, individual organizational and technical problems in the context of this topic were analyzed, the best foreign practices of using innovations in the field of environmental protection were outlined, directions for the development and adaptation of information technologies in the detection and investigation of criminal offenses were determined, taking into account the specifics of Ukrainian legislation and the practice of its application, to ensure sustainable and harmonious development of the country's ecological security. Analysis of the experience of countries such as the USA, Canada, Germany, and Great Britain has proven the impact of the latest technologies on the effectiveness of detecting and countering criminal offenses against the environment. It was concluded that modern technologies, including geo-information systems, electronic accounting systems, mobile applications for the public, automated emissions tracking systems, unmanned aerial vehicles and other innovative solutions are able to ensure the transparency of enterprises' activities and the active involvement of citizens in the control process, will allow prompt response to violations , to significantly increase the effectiveness of the actions of law enforcement agencies of Ukraine, in particular, in collecting evidence of illegal activities in the field of the environment. It was emphasized that taking into account the current environmental challenges in our country and the requirements for increasing the efficiency of law enforcement activities, the introduction and improvement of relevant innovative technologies in the field of environmental protection is urgent and expedient. This will not only improve the effectiveness of combating criminal offenses against the environment, but will also stimulate the appropriate attitude of business to environmental standards, while strengthening the public's trust in the actions of power structures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".