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Record W7065403425

Enhancing connectivity through corridors for the dispersal and biodiversity conversation of forest herbaceous species in agroecosystems

2011· dissertation· en· W7065403425 on OpenAlexfundaboutno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2011
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsBiological dispersalAgroecosystemBiodiversitySeed dispersalRange (aeronautics)Landscape connectivityHerbaceous plantFragmentation (computing)Climate change
DOInot available

Abstract

fetched live from OpenAlex

Existing stresses of fragmentation on plant diversity and range dynamics will be exacerbated by climatic changes that shift bioclimatic regions northward.It is often stated that connectivity-enhancement through the establishment of corridors could facilitate the movement and conservation of plants, providing a timescale of dispersal and establishment relevant to rapid bioclimatic change.We tested this hypothesis focusing on forest herbaceous species dispersing through agroecosystems in southern Quebec using regenerating hedgerow-corridors.Most species (n= 31 of 42) recorded in regional forests were also found in hedgerows, though species rates of dispersal through hedgerows, recorded at ≤ 2.50 m year -1 , indicate corridors would not benefit the dispersal of these species in rapidly changing conditions.Forest species tend to reassemble in corridors with time, but our results suggest that establishing connectivity would only conserve agroecosystem plant diversity at long timescales; dispersal limitations and possibly environmental conditions represent a severe barrier for most species.

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)0.001
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 score0.090
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.235
Teacher spread0.215 · 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
Published2011
Admission routes2
Has abstractyes

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