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Record W165088680

Giant Pandas: Biology, Veterinary Medicine, and Management

2007· article· en· W165088680 on OpenAlexaboutno aff
Graham J. Crawshaw

Bibliographic record

VenuePubMed Central · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic and phenotypic traits in livestock
Canadian institutionsnot available
Fundersnot available
KeywordsIUCN Red ListCaptive breedingEndangered speciesAiluropoda melanoleucaThreatened speciesZoologyConservation biologyBiologyHabitatEcology
DOInot available

Abstract

fetched live from OpenAlex

The giant panda, probably the most recognizable of any species of wild animal, is seriously threatened in its native habitat in China. Pandas exist in zoos and in reserves in China but only a small number have ever been seen in Europe or North America. Studying such an elusive and rare creature in its mountainous habitat is a difficult task, but the opportunity to increase knowledge of the biology of the animal can be gained by studying animals in breeding centers and zoos. There is a paucity of well-documented reports in English of medical data and the diseases that affect giant pandas. From 1998 to 2000, teams of biologists, veterinarians, reproductive specialists and geneticists, under the umbrella of the Conservation Breeding Specialist Group of the IUCN-World Conservation Union, undertook an intensive study of more than 60 captive pandas in collaboration with animal managers and scientific colleagues from conservation and research centers in China. This book represents the result of those examinations combined with data collected from other studies of both captive and free-living animals. This is an exceptional publication presenting a wealth of current knowledge on giant panda biology, including health, behavior, reproductive physiology, genetics, and species management. Although taxonomically a bear, the giant panda demonstrates many features not typical of other bears, in particular those related to its unusual diet. The 22 chapters cover topics such as genetics, social behavior, nutrition, clinical findings, clinicopathological data, diseases and pathology, and the results of ultrasonographic, gastroscopic, and colonoscopic examinations. Several sections are devoted to reproduction, including normal reproductive physiology and endocrinology, as well as assisted reproductive techniques. For many years successful reproduction of captive pandas was a rare event — females are sexually receptive for just 3 days a year, and this, coupled with issues of incompatibility between prospective pairs, meant few giant panda births. However, knowledge of panda reproduction has increased greatly in the past decade resulting in a dramatic increase in the number of panda cubs now being born and surviving. Artificial insemination has resulted in live births, while artificial rearing and switching cubs has enabled panda keepers to raise rejected neonates, as well as twins when typically only one of a pair would survive. Much of this progress has been made by the Chinese themselves, but the techniques and their precision have been refined with the assistance of veterinarians and scientists from outside the country. The book, which is written and edited very well, includes the results of the survey in detail, along with data tables, photographs and up-to-date reference lists. As more giant pandas become available for study, further knowledge on their biology and medicine will be gained, including perhaps the cause of their various digestive diseases, and a stunting syndrome that was recognized in 15% of the pandas examined. While this book may be of limited interest to most Canadian veterinarians, it is essential reading for those seeking information on the medical care, reproduction, and other aspects of the biology of this appealing animal. It also demonstrates the tremendous rewards to be gained from multidisciplinary and multinational projects despite considerable political, logistical, and linguistic challenges, as well as the importance of documenting procedures in new species.

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)0.000
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 score0.760
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.019
GPT teacher head0.256
Teacher spread0.237 · 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

Citations4
Published2007
Admission routes1
Has abstractyes

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