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Record W4386000152 · doi:10.31219/osf.io/rg59a

Do conservation translocations involve or result in hybridization and what are the consequences of hybridization for conservation? A systematic review protocol

2023· review· en· W4386000152 on OpenAlexaff
Kristen Vlahiotis, Carolyn J. Hogg, Axel Moehrenschlager, Beth Shaprio, Tammy E. Steeves, Carles Vilà, Yolanda van Heezik, Kiyoko M. Gotanda

Bibliographic record

Venuenot available
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic diversity and population structure
Canadian institutionsUniversité de SherbrookeBrock University
Fundersnot available
KeywordsThreatened speciesScopusIUCN Red ListBiologyGeographyEcologyMEDLINE

Abstract

fetched live from OpenAlex

BackgroundConservation translocations are the intentional movement of plants and animals to another location for the purpose of conservation (IUCN/SSC, 2013). Conservation translocations can have intended and unintended impacts on threatened populations. Conservation translocations can be beneficial, harmful, or both and such impacts can be intended or unintended (Novak et al., 2021). Conservation translocations can result in or involve hybridization. Hybridization is defined as the interbreeding of individuals from genetically distinct populations and can be intentional or unintentional. Hybridization can be interspecific, intraspecific or between subspecies (Chan et al., 2019). For conservation practitioners, understanding if hybridization might occur and what the consequences are is a key consideration when developing conservation management policies. There is currently no systematic review of (A) the frequency of hybridization after conservation translocations and (B) the consequences of hybridization occurring after conservation translocations. This review will provide critical insight and information for conservation professionals and scientists who are considering conservation translocations. MethodsA thorough search of peer-reviewed journal articles, open-access articles, and grey literature will be conducted. Five different databases will be searched, one open access (BASE) and four subscription based (Web of Science Core Collection, Zoological Record, Scopus, and BIOSIS). Bibliographic information will be recorded using the citation management software Zotero. Two screening stages will be performed (title/abstract, then full text) against predefined inclusion and exclusion criteria. The retained relevant literature will be subjected to coding and meta-data extraction. Each relevant study will then be critically appraised, based on a set of predefined validity criteria. Relevant knowledge gaps and emergent themes will be identified and discussed further.

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)0.071
metaresearch head score (Gemma)0.077
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.071
Threshold uncertainty score0.375

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0710.077
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0140.013
Bibliometrics0.0240.015
Science and technology studies0.0040.005
Scholarly communication0.0060.008
Open science0.0050.005
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0620.008

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 head0.095
GPT teacher head0.367
Teacher spread0.272 · 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 designSystematic review
Domainnot available
GenreProtocol

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
Published2023
Admission routes1
Has abstractyes

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