The Main Threats in the Practice of a Lawyer to Ensure Environmental Safety in the Context of COVID-19
Bibliographic record
Abstract
The main purpose of the study is to determine the main ways to counter environmental threats, taking into account the impact of COVID-19 in the practice of a modern lawyer. To achieve this goal, we used the methodology of functional modeling and graphical display to represent the key stages and processes of counteracting the negative impact of environmental threats. Among the global problems of our time, one of the central cities occupies the issue of proper environmental protection, taking into account the peculiarities of the whole variety of its components and the impact of COVID-19. Conservation of natural resources, along with environmental well-being, is the determining factor in the comfort of human existence, ensuring the sustainability of social and economic development. The concept of harm to the environment and legal liability for such harm at the scientific level began to be developed relatively recently, which determined the relevance of the chosen issue. As a result of the study, a methodological approach was proposed to reflect the main measures to counter environmental threats on the part of practicing lawyers.
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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 | 0.018 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".