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Record W4387704940 · doi:10.3389/fevo.2023.1261265

Biodiversity conservation and ecological value of protected areas: a review of current situation and future prospects

2023· review· en· W4387704940 on OpenAlexaboutno aff
Xiangyu He, Hongbin Wei

Bibliographic record

VenueFrontiers in Ecology and Evolution · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
FundersNational Key Research and Development Program of China
KeywordsBiodiversityEnvironmental resource managementConnotationBiodiversity conservationMeasurement of biodiversityEcological healthValue (mathematics)EcologyGeographyEcosystem servicesEcosystemEnvironmental scienceBiologyComputer science

Abstract

fetched live from OpenAlex

The establishment of protected areas (PAs) is an effective way to biodiversity conservation while maintaining the multiple functions of ecosystem services. However, there is still a lack of comprehensive analysis on the relationship between PAs, biodiversity conservation, and ecological value in the field of research on prospects. Therefore, based on the research progress of literature content, this paper systematically reviews and evaluates domestic and international studies in terms of the biodiversity conservation and the ecological value of PAs. The results showed that relevant studies in recent years have mainly analyzed the spatial layout, area changes and conservation effectiveness of PAs in relation to biodiversity; By constructing the connotation system of ecological value concept of PAs, the research progress of ecological value accounting, ecological value realization and transformation, and ecological value types of PAs is further discussed, and the prospects of biodiversity conservation and ecological value research of PAs is proposed. This study provides a reference for the implementation of the Kunming-Montreal Global Biodiversity Framework and the construction and optimization of PAs system.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.197
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations14
Published2023
Admission routes1
Has abstractyes

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