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Record W4393407728 · doi:10.18599/grs.2024.1.6

Comparative Analysis of Approaches to the Formation of an Institutional Framework for the Development of a Changing Resource Base for Hydrocarbon Production (on the Example of High-viscosity Oils in Alberta (Canada) and the Republic of Tatarstan (Russia))

2024· article· en· W4393407728 on OpenAlexaboutno aff
V. А. Kryukov, Yulia A. Borisova

Bibliographic record

VenueGeoresursy · 2024
Typearticle
Languageen
FieldEnergy
TopicCoal and Coke Industries Research
Canadian institutionsnot available
Fundersnot available
KeywordsResource (disambiguation)Production (economics)Base (topology)Petroleum engineeringEnvironmental resource managementEngineeringEnvironmental scienceComputer scienceEconomicsMathematics

Abstract

fetched live from OpenAlex

The modern evolution of the dynamics of the structure of the hydrocarbon resource base in various regions is characterized, as a rule, by the gradual replacement of traditional sources with more complex ones. The latter are currently classified as hard-to-recover reserves (HTRR). The process of mastering and involvement in the development of HTRR is closely related to the transition to an innovative path of development, the creation of new technologies, the strengthening of the role of local knowledge, the accumulation of experience working with non-trivial sources of raw materials, and most importantly, with the efforts cooperation of various participants involved both in scientific and technological processes and in the development of subsurface areas. All of the above is impossible without the formation of an appropriate institutional framework with regional specifics. One example of an approach implemented in this area is the Canadian province - Alberta. There is also some experience in this field in Russia – in the Republic of Tatarstan. In a comparison with the initial conditions and approaches to the development of hard-to-recover hydrocarbon resources in these territories shows that there are both common and distinct features within the framework of the implemented approaches. Consideration of the latter is important from the point of view of choosing the directions for the formation a domestic working model in this area.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.257

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0030.010
Scholarly communication0.0070.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.120
GPT teacher head0.281
Teacher spread0.161 · 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 designTheoretical or conceptual
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

Citations3
Published2024
Admission routes1
Has abstractyes

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