ANIMAL TRAPS - EU AND NATIONAL REGULATIONS WITH SPECIFIC EMPHASIS ON THE AGREEMENT ON INTERNATIONAL NORMS OF HUMANE ANIMAL CAPTURE
Bibliographic record
Abstract
Animal traps have always accompanied man, with whom the primary people organized the first hunts. Along with the development of hunting art, traps gradually gave way to specialized hunting weapons. However, the use of animal traps on a large scale still occurs in countries that are world exporters of fur and skins of wild animals - Canada, Russia and the USA. Driven by expressed in art. 13 TFEU with the principle of animal welfare, the European Union has introduced a number of regulations to ensure humane catches in member countries as well as in third countries exporting skin and fur. The purpose of this article is to analyze the current legal situation in Poland with regard to the implementation of EU legislation on humane trap standards, with particular regard to the obligations contained in the agreement concluded between the European Community, Canada and the Russian Federation on 22 July 1997 - on international humane trapping standards . Keywords - EU, Poland, Russia, Canada, USA, animal welfare, humane animal protection, snare, poaching, animal species protection, hunting, animal traps, hunting, trapping, hunting law.
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.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".