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Record W2991311360 · doi:10.1051/e3sconf/201913101035

Protection and enlightenment of ecological integrity of Canadian national parks

2019· article· en· W2991311360 on OpenAlexaboutno aff
Shuhui Yang, Xiaoyu Duan

Bibliographic record

VenueE3S Web of Conferences · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicLand Use and Ecosystem Services
Canadian institutionsnot available
FundersSichuan Agricultural University
KeywordsNational parkEnvironmental resource managementChinaEnlightenmentEcosystem managementSustainable developmentAdaptive managementNature reserveGeographyEnvironmental planningEnvironmental protectionEcosystemBusinessEcologyEnvironmental science

Abstract

fetched live from OpenAlex

Canada is one of the first countries in the world to establish a national park, and pioneered the concept of ecological integrity management of national parks. Based on this concept, the country has basically achieved the sustainable development of national parks. China has a vast territory, a large number of scenic spots and nature reserves, but its system and management methods need to be optimized. This paper takes forestry developed countries as an example, summarizes the progress of ecological integrity protection in Canadian national parks, summarizes its current ecosystem adaptive management concepts and implementation methods, Ecological Integrity (EI) monitoring construction and related evaluation index systems, ecosystem protection and restoration. The experience is intended to provide a reference for the improvement of the ecological integrity protection of national parks in China.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.090
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0070.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.022
GPT teacher head0.215
Teacher spread0.194 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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