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Record W2726334682

Desafios na conservação de vida selvagem: perspectivas presentes e futuras

2015· article· pt· W2726334682 on OpenAlexaboutno aff
Nucharin Songsasen

Bibliographic record

VenueRevista Brasileira de Reprodução Animal · 2015
Typearticle
Languagept
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsnot available
Fundersnot available
KeywordsIUCN Red ListThreatened speciesCaptive breedingWildlifeBiologyPopulationNear-threatened speciesEcologyBiodiversityEndangered speciesFisheryZoologyGeographyHabitatDemography
DOInot available

Abstract

fetched live from OpenAlex

The global environment is under increasing pressure from expanding human activities and climate changes. The change in global environment, including increase environmental temperature, marine pollution, ocean acidification among others, has resulted in significant loss in biodiversity. To dates, the International Union for Conservation of Nature (IUCN) has estimated that 41% of amphibians, 33% of corals, 26% of mammals and 13% of birds are threatened by extinction (International Union for Conservation of Nature - IUCN, 2015). Imminent extinction of wild species is often caused by multiple factors and may not always be due to failure of animals to breed. Nevertheless, reproductive sciences play critical roles in wildlife conservation, especially captive breeding program. A clear example of how reproductive biology contributes to species recovery program is the case of black footed ferret (Howard et al., 2003; Santymire et al., 2014), endemic to North America. In 1980s, the species underwent severe population decline with only 18 individuals remained in the wild which were brought into captivity. To-date, >150 ferret kits have been produced by artificial insemination (AI), including offspring produced from frozen founder sperm stored for as long as 20 years. Since the inception of the captive breeding program, 8,000 black footed ferrets have been produced, half of which have been reintroduced into 20 sites in eight US States, Canada and Mexico. Despite the success story of the black-footed ferret, the application of reproductive technologies to wildlife species is very limited. This is mostly due to the lack of basic knowledge on reproductive biology. As described in a review paper by Wildt et al. (2010), of 12,000 papers published in 10 leading reproductive journals, only 6% were dedicated to mammals (non-traditional species), 3% for fishes and <1% for amphibians, birds and reptiles. Without thorough understanding of reproductive biology, it will be very difficult to apply reproductive technologies to a given species on a regular basis. While there are some success in wild felids (Swanson, 2012), so far, there has not been a single example of embryo-based technologies being consistently utilized in species recovery program. More research on basic reproductive biology is needed for embryo technology can be incorporated into species recovery program. Furthermore, mechanisms for reproduction are as diverse as animals are in physical appearance, genotype or geographic origin (Wildt et al, 2010). Examples of reproductive diversity have been recently reviewed for carnivores by Jewgenow and Songsasen (2014). Basically, reproductive mechanisms of animals within the same taxon are not always the same. For example, female maned wolves, unlike other canid species, require the presence of a male conspecific for ovulation to occur (Johnson et al., 2014). Ovulation induction in this species is believed to be regulated by chemical signals (Kester et al 2015). Because of the high diversity in reproductive mechanisms among species, ones cannot directly apply protocol developed from one species to another. This presentation will provide an overview of global threats to wildlife, conservation challenges, specific examples of success and failure stories and future priorities for successful application of reproductive technologies to wildlife conservation.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0040.008
Scholarly communication0.0110.012
Open science0.0030.004
Research integrity0.0070.009
Insufficient payload (model declined to judge)0.0110.002

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 head0.042
GPT teacher head0.284
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2015
Admission routes1
Has abstractyes

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