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Record W2355205189

The assessment of regional ecological risks of wind energy exploitation in the mountain area based on landscape ecology

2014· article· en· W2355205189 on OpenAlexaff
Liu Zh

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Acceptance of Renewable Energy
Canadian institutionsScience North
Fundersnot available
KeywordsLandscape ecologyEcologyGeographyFunctional ecologyEnvironmental resource managementEnvironmental scienceHabitatEcosystem
DOInot available

Abstract

fetched live from OpenAlex

To assess regional ecological risk of wind energy exploitation in the mountain area. Taking the landscape ecology as a point,considering the function and the anti-interference ability of different types of landscape in the ecological environment,isolation effect of wind energy exploitation on the regional landscape, meanwhile,combining with mountain surface,geological environment,regional landscape component,and the characteristic of wind energy exploitation,three indexes of landscape risk potential,isolation effect,ecological sensitive coefficient were put forward to design the evaluation model of regional ecological risks of wind energy exploitation in the mountain district. If the design was put into practice,73. 44% of the area will be intermediate risk and below area; less than 5% of the area will be high risk area or extremely high risk area,which is in the northeast of W1 and the south of W14-W22 section. Wind energy exploitation will increase regional ecological risk,and the risk change trend will appear obvious ridge orientation features; although the sphere of high risk influence is limited,the neighboring human activities esselte regions will be greatly effected.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.058
GPT teacher head0.353
Teacher spread0.296 · 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
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

Citations1
Published2014
Admission routes1
Has abstractyes

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