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Priorities for protected area research

2018· paratext· en· W4234800492 on OpenAlexaff
Nigel Dudley, Marc Hockings, Sue Stolton, Thora Amend, Ruchi Badola, Mariasole Bianco, Carly N. Cook, Jon Day, Philip Dearden, Mary E. Edwards, Paul J. Ferraro, Wendy Foden, Roberto Gambino, Kevin J. Gaston, Natalie Hayward, Valerie Hickey, Jason Irving, Bruce Jeffries, A.P. Karapetyan, Lars Laestadius, Dan Laffoley, Dechen Lham, Gabriela Lichtenstein, John Makombo, Nina Marshall, Mélodie A. McGeoch, Dao Nguyen, Sandra Nogué, Midori Paxton, Madhu Rao, Russell Reichelt, Jorge Rivas, Dirk J. Roux, Kate Schreckenberg, Andrej Sovinc, Svetlana Sutyrina, Agus Utomo, Daniel Vallauri, Pål Vedeld, Bas Verschuuren, John Waithaka, Stephen Woodley, Carina Wyborn, Yan Zhang

Bibliographic record

VenuePARKS · 2018
Typeparatext
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsUniversity of Victoria
FundersNational Marine Fisheries ServiceDirektion für Entwicklung und ZusammenarbeitUniversity of Hong KongNational Oceanic and Atmospheric AdministrationCentral European UniversitySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsComputer science

Abstract

fetched live from OpenAlex

A clearly defined geographical space, recognised, dedicated and managed, through legal or other effec ve means, to achieve the long-term conserva on of nature with associated ecosystem services and cultural values.The defini on is expanded by six management categories (one with a sub-division), summarized below.Ia Strict nature reserve: Strictly protected for biodiversity and also possibly geological/ geomorphological features, where human visita on, use and impacts are controlled and limited to ensure protec on of the conserva on values.Ib Wilderness area: Usually large unmodified or slightly modified areas, retaining their natural character and influence, without permanent or significant human habita on, protected and managed to preserve their natural condi on.II Na onal park: Large natural or near-natural areas protec ng large-scale ecological processes with characteris c species and ecosystems, which also have environmentally and culturally compa ble spiritual, scien fic, educa onal, recrea onal and visitor opportuni es.III Natural monument or feature: Areas set aside to protect a specific natural monument, which can be a landform, sea mount, marine cavern, geological feature such as a cave, or a living feature such as an ancient grove.IV Habitat/species management area: Areas to protect par cular species or habitats, where management reflects this priority.Many will need regular, ac ve interven ons to meet the needs of par cular species or habitats, but this is not a requirement of the category.V Protected landscape or seascape: Where the interac on of people and nature over me has produced a dis nct character with significant ecological, biological, cultural and scenic value: and where safeguarding the integrity of this interac on is vital to protec ng and sustaining the area and its associated nature conserva on and other values.VI Protected areas with sustainable use of natural resources: Areas which conserve ecosystems, together with associated cultural values and tradi onal natural resource management systems.Generally large, mainly in a natural condi on, with a propor on under sustainable natural resource management and where low-level non-industrial natural resource use compa ble with nature conserva on is seen as one of the main aims.The category should be based around the primary management objec ve(s), which should apply to at least three-quarters of the protected area -the 75 per cent rule.The management categories are applied with a typology of governance types -a descrip on of who holds authority and responsibility for the protected area.IUCN defines four governance types.Governance by government: Federal or na onal ministry/agency in charge; sub-na onal ministry/agency in charge; government-delegated management (e.g. to NGO) Shared governance: Collabora ve management (various degrees of influence); joint management (pluralist management board; transboundary management (various levels across interna onal borders)

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)0.054
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.946
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0540.054
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.004
Science and technology studies0.0050.004
Scholarly communication0.0150.016
Open science0.0050.012
Research integrity0.0160.015
Insufficient payload (model declined to judge)0.0800.024

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 head0.081
GPT teacher head0.311
Teacher spread0.230 · 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.

Study designTheoretical or conceptual
DomainEvaluation
GenreCommentary

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

Citations3
Published2018
Admission routes1
Has abstractyes

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