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Record W2123946903 · doi:10.1111/emr.12041

Whose backyard? Some precautions in choosing recipient sites for assisted colonisation of <scp>A</scp>ustralian plants and animals

2013· article· en· W2123946903 on OpenAlexaff
Stephen Harris, Sophie G. Arnall, Margaret Byrne, David Coates, Matt W. Hayward, Tara G. Martin, Nicola J. Mitchell, Stephen T. Garnett

Bibliographic record

VenueEcological Management & Restoration · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsDepartment of Environment and Conservation
Fundersnot available
KeywordsColonisationThreatened speciesEcologyBiotaIUCN Red ListBiologyGeographyHabitatColonization

Abstract

fetched live from OpenAlex

Summary In cases where assisted colonisation is the appropriate conservation tool, the selection of recipient sites is a major challenge. Here, we propose a framework for site selection that can be applied to the Australian biota, where planning for assisted colonisation is in its infancy. Characteristics that will be important drivers in the decision‐making process include the size of a recipient site, the potential to augment corridors and respond to niche gaps, the maximisation of climatic buffering, bioregional similarity, tenure security, and the minimisation of opportunities for hybridisation and invasiveness. Sites we suggest be precluded from assisted colonisation include sites of high species endemism, IUCN category 1 reference reserves and fully‐functioning threatened ecological communities.

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)0.019
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: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.003
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.055
GPT teacher head0.274
Teacher spread0.219 · 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

Citations13
Published2013
Admission routes1
Has abstractyes

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