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Botanic Gardens in Biodiversity Conservation and Sustainability: History, Contemporary Engagements, Challenges and Renewed Potential

2024· preprint· en· W4392756702 on OpenAlexaff
Katja Neves

Bibliographic record

VenuePreprints.org · 2024
Typepreprint
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsConcordia University
Fundersnot available
KeywordsSustainabilityBiodiversityBiodiversity conservationEnvironmental planningEnvironmental resource managementEnvironmental ethicsGeographyAgroforestryBusinessEcologyEconomicsEnvironmental scienceBiologyPhilosophy

Abstract

fetched live from OpenAlex

A burgeoning body of scholarship identifies and discusses botanic gardens as increasingly important centres of biodiversity conservation and sustainability. Notwithstanding the high quality and richness of this literature, it encompasses relatively autonomous fields of expertise that are neither designed nor expected to promote cross-disciplinary, integrative, accounts of botanic gardens as institutions of conservation and sustainability. Bridging key aspects of the botanic garden literature, this article brings into conversation historical accounts with contemporary scholarship on the matter. In so doing, it unveils dilemmas and challenges faced by botanic gardens as they grapple with their historic legacies, but also renew their relevance in nurturing sustainable socio-environmental futures. The article proceeds by covering three focal points. First, it summarizes the scholarly literature on the history of botanic gardens. Second, it presents accounts of current scientific and biodiversity conservation endeavours as reflexive engagements with their historical legacies. Third, it addresses the recent emergence of socio-cultural missions at botanic gardens as a significant step beyond their historical focus on plants and plant ecosystems.

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)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.082

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.005
Science and technology studies0.0100.026
Scholarly communication0.0120.012
Open science0.0010.009
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0070.000

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 head0.101
GPT teacher head0.261
Teacher spread0.161 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2024
Admission routes1
Has abstractyes

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