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Record W4241880919 · doi:10.35735/tig.2019.14.76.005

ЗЕМЕЛЬНЫЕ РЕСУРСЫ ПРИБРЕЖНЫХ РАЙОНОВ ТИХООКЕАНСКОЙ РОССИИ (ТР): МЕЛКОМАСШТАБНАЯ ТИПОЛОГИЯ

2019· article· ru· W4241880919 on OpenAlex
V.P. Karakin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

Venuenot available
Typearticle
Languageru
FieldEnvironmental Science
TopicEnvironmental Sustainability and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsLand coverLand useEnvironmental resource managementGeographyNatural resourceChinaState of the EnvironmentScale (ratio)Environmental protectionEnvironmental planningEnvironmental sciencePolitical scienceCartographyEcology

Abstract

fetched live from OpenAlex

Оценки изменчивости географической среды крупных регионов одно из традиционных направлений Географии. При этом ряд исследователей, экологогеографического направления считают, что в настоящее время изучение нарушенности естественных экосистем (геосистем) одна из базовых проблем Географии. Состояние земельного покрова является одной из наиболее информативных характеристик при оценке изменений географической среды масштабных географических объектов. Относительно легко фиксируемый, лежащий на (земной) поверхности аспект трансформации естественных экосистем/геосистем это изменение структуры земельного покрова и связанной с этим системы землепользования. Практическая реализация данного подхода для мелкомасштабной оценки крупных регионов предполагает использование информации о состоянии земельного покрова, которая отвечает ряду требований. Информация должна быть, в первых однородной по методу получения, во вторых систематически обновляться. В большинстве стран этим требованиям отвечает в максимальной мере информация, которую продуцируют структуры ответственные за ведение Государственного Земельного Кадастра. В России это Росреестр в Канаде Canada Land Inventory, в КНР Ministry of Land and Resources of the Peoples Republic of China и др. Для формирования генерализованного представления о земельном покрове важен метод интеграции земельноресурсной информации, которая может быть получена при использовании данных Государственного земельного Кадастра. При мелкомасштабной характеристике земельных ресурсов береговой зоны Тихоокеанской России использовался метод выделения типов структур земельных ресурсов по административным районам на основании данных Государственного Земельного Кадастра. Monitoring and studying the dynamics of the state of the geographical space is a traditional direction of geographical research, which is carried out at various scale levels. In contemporary conditions, with the intensification of the processes of degradation of natural ecosystems (landscapes), the study of the disturbance of natural ecosystems and dynamics of habitats is becoming increasingly important, especially at the smallscale level. The state of land cover is one of the most informative characteristics in assessing changes in the geographic environment in the course of smallscale geographical assessments. Changes in the structure of land cover and the associated land use system are reflected in the state land inventory statistics. Practical implementation of a smallscale assessment of large regions involves the use of information on the state of land cover, which meets several requirements. Firstly, information should be homogeneous according to the method of its sourcing secondly it should be systematically updated. In most countries, the information produced by the institutions responsible for maintaining the state land inventory meets these requirements. It is Rosreestr in Russia, Canada Land Inventory in Canada, Ministry of Land and Resources in the Peoples Republic of China, and so on. Smallscale assessments of lands means a creation of a generalized image of the land cover of the study area, which can be based on integration of available landresource data from the State Land Inventory. Smallscale characteristics of land resources by administrative districts were used to define various types of land resources patterns in coastal areas of Pacific Russia. Selecting the enlarged types of land resource patterns enables to create an overview map that reflects the general patterns of the spatial differentiation of land cover of the area under study.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.2220.093

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 head0.002
GPT teacher head0.174
Teacher spread0.172 · 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