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Record W2165639712 · doi:10.2980/19-1-3460

An orchid colonization credit in restored calcareous grasslands

2012· article· en· W2165639712 on OpenAlexvenueno aff
Pieter Gijbels, Dries Adriaens, Olivier Honnay

Bibliographic record

VenueEcoscience · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsGrasslandColonizationEcologyExtinction debtBiologyHabitatHabitat fragmentationCalcareousEcological successionAbundance (ecology)Habitat destructionBotany

Abstract

fetched live from OpenAlex

Habitat restoration comprises the re-creation of suitable environmental conditions with the intention of recolonization by certain target species. In previously abandoned calcareous grasslands, however, many characteristic plant species have been reported missing even decades after the reinstatement of traditional mowing or grazing management. Such grasslands are said to exhibit a colonization credit. This may be particularly true for orchid species, which often rely on highly specialized pollination strategies and mycorrhizal associations for completion of their life-cycle. In this study, we investigated whether restored calcareous grasslands exhibited an orchid colonization credit, whether this credit was associated with the degree of grassland fragmentation, and with particular species' life history traits. Applying the Beals index as a quantitative method to identify suitable habitats, several orchid species were indeed found missing from grasslands deemed suitable. There was no relation, however, between the extent of the colonization credit and the spatial isolation or size of the grasslands. Of all life history traits examined, only a high degree of pollinator specialization could be related to delayed colonization. This may suggest that restoring the pollinator community is an important bottleneck in calcareous grassland restoration.

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 score0.198
Threshold uncertainty score0.198

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 head0.044
GPT teacher head0.235
Teacher spread0.190 · 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

Citations28
Published2012
Admission routes1
Has abstractyes

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