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Record W7095509088

RESEARCH ARTICLE The Distribution of Cool Spots as Microrefugia in a Mountainous Area

2016· article· en· W7095509088 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicIrish and British Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHabitatAlpine plantClimate changeVolcanoDistribution (mathematics)Vegetation (pathology)Species distributionPlant community
DOInot available

Abstract

fetched live from OpenAlex

It has recently been proposed that microrefugia played an important role in species survival during past climate change events. However, the current distributions of microrefugia remain largely unknown. Wind-hole sites are areas affected by preferential flows of cool air generated in interstitial spaces created by rock fragments or colluvia. Alpine plant species occurring in lowland wind-hole sites isolated from alpine zones may be relicts of the last gla-cial period. Hokkaido, northern Japan, is known to contain many wind-hole sites in which alpine plant species can occur. Here we surveyed 55 wind-hole sites in the Kitami region, eastern Hokkaido, and observed two alpine plant species (lingonberry, Vaccinium vitis-idaea, and Labrador tea, Rhododendron groenlandicum ssp. diversipilosum var. diversipi-losum) in 14 wind-hole sites. Statistical modeling showed that wind-hole sites are likely to occur in areas with high maximum slope angles and volcanic rock cover, and concave sur-faces. Our predictions of wind-hole site distributions suggest that such topographic condi-tions are common in our study area, and that many undiscovered wind-hole sites exist. Ignoring microhabitats may greatly underestimate species distributions in topographically complex regions, and dispersed cool spots may also function as stepping stones and tem-poral habitats for cold-adapted species. Because these localized unique habitats usually occur in economically unproductive sites, identifying and protecting potential microrefugia (cool spots) would be a robust and cost-effective mitigation of climate change impacts.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.038
GPT teacher head0.359
Teacher spread0.321 · 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 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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same topicIrish and British StudiesFrench-language works237,207