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Biodiversity and Protected Areas

2019· book· en· W3197105281 on OpenAlexafffundabout
Karen Beazley, Robert F. Baldwin

Bibliographic record

Venuenot available
Typebook
Languageen
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsDalhousie University
FundersNational Centre for Earth ObservationNatural Environment Research CouncilDalhousie UniversityUniversity of LeicesterRoyal SocietyWalton Family Foundation
KeywordsBiodiversityContext (archaeology)GeographyProtected areaBiodiversity conservationEnvironmental resource managementEnvironmental ethicsEcologyArchaeologyEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Biodiversity and Protected Areas assembles twelve topics from around the world, illustrating the complexities and promise of addressing the biodiversity crisis. Authors from Mongolia, Africa, India, Canada, Iraq, and the United States dwell on particular aspects and challenges relevant to those regions. Lessons and approaches from interesting localities, coupled with global analyses give the reader a synthetic view of emerging problems. The opportunities for understanding common issues across different geographies abound, such as comparing local conservation in sub-Saharan Africa with a distribution of very small protected areas in Massachusetts. Several topics will be of immediate interest to policymakers. The book is illustrated with numerous color maps and figures and the authors strove for clear, uncomplicated writing. The editors provide an overview of chapters, placing them in the context of other biodiversity and protected area literature. Students and conservationists attempting to broaden their views of biodiversity and protected areas should find this collection to be interesting.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.113
Threshold uncertainty score0.379

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1130.042

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 head0.009
GPT teacher head0.162
Teacher spread0.153 · 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
GenreOther

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
Published2019
Admission routes3
Has abstractyes

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