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Assessment of the dynamics of biodiversity of green spaces of Krasnoyarsk city

2020· article· en· W2999004733 on OpenAlexaboutno aff
O S Artemiev, А. А. Вайс, E A Vyazmina, Г. С. Вараксин, В И Незамов

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil and Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiodiversityQuarter (Canadian coin)Research ObjectGeographyUrban green spaceWork (physics)Resistance (ecology)Environmental qualitySpace (punctuation)Environmental protectionAgroforestryEcologyEnvironmental scienceRegional scienceBiologyEngineeringArchaeology

Abstract

fetched live from OpenAlex

Abstract In large Russian cities there is a significant decrease in urban plantations, which negatively affects the environmental situation. In recent years, there has been a growing scientific interest in assessing biodiversity of green spaces. As a result, the analysis of the dynamics of the number of trees and shrubs in urban areas is a very relevant and practical issue. The work is based on research results for 28 years. The object of research was the green space of the city of Krasnoyarsk. An analysis of the assortment of intra-quarter plantations and a survey of the territory of the quarters showed that mainly the decrease in the number of trees and shrubs was caused not by negative environmental factors, but by the point development of the territory. As a result, it can be stated that the dynamics of green plant biodiversity in the urban environment is caused by a number of processes that can be either negative (reducing plant resistance, decreasing area) or positive (increasing the assortment of plants, improving the quality of care for green spaces).

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
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 head0.021
GPT teacher head0.184
Teacher spread0.164 · 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.

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
Published2020
Admission routes1
Has abstractyes

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