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Assessment of Potential Karner Blue Butterfly (<i>Lycaeides melissa samuelis</i>) (Family: Lycanidae) Reintroduction Sites in Ontario, Canada

2006· article· en· W2152495527 on OpenAlexaffabout
Pak Kin Chan, Laurence Packer

Bibliographic record

VenueRestoration Ecology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsYork University
FundersWorld Wildlife Fund
KeywordsButterflyEndangered speciesHabitatEcologyAbiotic componentRestoration ecologyBiology

Abstract

fetched live from OpenAlex

Abstract Although species reintroduction is useful as both an integral part and a performance indicator of habitat restoration, it is not a risk‐free process. Evaluation of potential reintroduction sites is often crucially important in reducing chances of failure. Despite its importance, there is no standard way to do so. This study applied a systematic scheme to evaluate five potential reintroduction sites for the endangered Karner Blue butterfly ( Lycaeides melissa samuelis Nabokov) in Ontario, Canada, by looking at both biotic and abiotic aspects. Field data were collected in 2003 from these sites, and three potential founder butterfly sites in the United States. We used data collected from the U.S. sites to determine the minimum standards for ecological requirements of the Karner Blue. Data from the Ontario sites were then compared against these standards. The results show that all five potential reintroduction sites are of lower quality, at least in certain aspects, compared with the three potential founder butterfly sites. This implies that success is not guaranteed if the Karner Blue is reintroduced into these sites under current habitat conditions. Further site restoration is required and should focus on the shortcomings identified for individual sites in this study. The lessons from this study are useful for potential reintroduction site assessment in other restoration projects because they reveal whether the sites are ready for species reintroduction and, if not, how they need to be improved.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score0.215

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 head0.017
GPT teacher head0.200
Teacher spread0.183 · 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

Citations22
Published2006
Admission routes2
Has abstractyes

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