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Record W4246000356 · doi:10.24124/2020/59040

Plant communities of the grasslands and the forest-grassland mosaic in the Cariboo-Chilcotin region of British Columbia

2020· dissertation· en· W4246000356 on OpenAlexaffabout
Nicholas Paul Hamilton

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsThompson Rivers UniversityUniversity of Northern British Columbia
Fundersnot available
KeywordsGrasslandGrassland ecosystemPlant communityAgroforestryGeographyVegetation (pathology)EcosystemEnvironmental scienceSpecies richnessEcologyBiology

Abstract

fetched live from OpenAlex

Plant communities and soils of grasslands on an elevation gradient and the forest-grassland mosaic in the Cariboo-Chilcotin of British Columbia were examined. Vegetation change along an elevation-climate gradient was analysed, including plant cover, litter cover, species composition, and species richness. Grassland plant communities were compared with associated climate variables and biogeoclimatic classifications. Based on results, an updated classification of grassland ecosystems is proposed with suggestions for management and research. Plant community and soil moisture characteristics of adjacent forests and grasslands were compared. Ten-year-old slash and burn treatments of young forests, and areas of forest encroachment on grasslands were studied to detect potential change between forest and grassland states. Results did not suggest change in ecosystem state between grasslands and forests, with persistent ecosystem legacies a potential source of long-term resilience. Land-use legacies manifested as encroachment of forests on areas of historical grasslands and high cover values of exotic 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)0.000
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 score0.055
Threshold uncertainty score0.110

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
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 head0.008
GPT teacher head0.182
Teacher spread0.175 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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