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Record W2151813030 · doi:10.5539/ijb.v3n1p136

The Problems and Countermeasures of Animal Protection in Zoos--Take Shenyang Glacier Zoo for Example

2010· article· en· W2151813030 on OpenAlexvenueno aff
Bingbing Cui, Dezhong Jiang

Bibliographic record

VenueInternational Journal of Biology · 2010
Typearticle
Languageen
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsnot available
Fundersnot available
KeywordsRecreationScale (ratio)GlacierEnvironmental ethicsEndangered speciesPolitical scienceEnvironmental protectionGeographyEcologyBiologyLawHabitatPhysical geographyCartography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.127

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.062
GPT teacher head0.342
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2010
Admission routes1
Has abstractyes

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