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Record W2197801454

Real Estate in the Russian Federation

2012· article· en· W2197801454 on OpenAlexaboutno aff
Georgy Zadonsky

Bibliographic record

VenueRussian Economic Developments · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEconomic and Technological Developments in Russia
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Real estateCurrencyRussian federationDebtBusinessAgricultural economicsRussian economyFinanceEconomicsGeographyMonetary economicsEconomic policyArchaeology
DOInot available

Abstract

fetched live from OpenAlex

In July 2012, entities of all the forms of ownership built 52, 600 new apartments with the total floorspace of 4.21m sq. meters which is 13.8% more than in July 2011. In the 2nd quarter of 2012, the ratio between the price of a sq. meter of housing on the primary market and the cost of building of a sq. meter of housing amounted to 147.42% against 138.9% in the 1st quarter of 2012. The growth in the outstanding debt on MHL in rubles continued, and as of August 1, 2012 it amounted to Rb 1, 572, 685bn which is 41.28% more than in August 1, 2011. As regards loans in foreign currency, as of August 1, 2012 the debt on MHL amounted to Rb 137, 728bn which is 11.7% less than that as of August 1, 2011.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.030
GPT teacher head0.311
Teacher spread0.281 · 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
Published2012
Admission routes1
Has abstractyes

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