The Problems and Countermeasures of Animal Protection in Zoos--Take Shenyang Glacier Zoo for Example
Bibliographic record
Abstract
With the continuous expansion of the scale of the zoo, living environment of animals is deteriorating. Taking theproblems appeared in Glacier Zoo in Shenyang as example, the author analyzed the reasons for the phenomenonand put forward corresponding countermeasures such as establishing animal protectors association and fund andcarrying out education for the protection et. al. ????Once defined as zoo with four functions including the education of science popularization, scientific research,animal reproduction protection and leisure and recreation, the establishment of them is to protect animals betterso that several species which are in weak positions or rare endangered get better protection and long-termdevelopment. Meanwhile, it also can enhance people’s understanding for animals’ habits so as to promote thecommunications between human beings and animals and provide materials for animal science research so as topromote the education of science popularization and cultivate people’s love. But with the increase of zoos andcontinuous expansion of its scale, the phenomena which the animals in zoos are abused appear constantly andbecome severe day by day and the living environment of animals is deteriorating. The article takes the problemsappeared in Glacier Zoo in Shenyang as example to analyze the reasons for the phenomena and put forwardcorresponding countermeasures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".