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Record W4400857633 · doi:10.34925/eip.2021.137.12.213

Audit of cost accounting in Arta LLC for the third quarter of 2021

2022· article· ru· W4400857633 on OpenAlexaboutno aff
Н.А. Илюшкина

Bibliographic record

VenueЭкономика и предпринимательство · 2022
Typearticle
Languageru
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)AuditAccountingBusinessHistory

Abstract

fetched live from OpenAlex

Актуальность проблемы грамотного учета затрат в организации объясняется периодическим повышением штрафов за неправильное исчисление налогов и сборов или несвоевременную их уплату. Затраты влияют на определение налогооблагаемой базы по налогу на прибыль организаций, а он является одним из важнейший в РФ и крупных по размеру. Наибольшее количество нарушений, которые регистрирует налоговая службы, касаются именно расчета налога на прибыль организаций и именно в части завышения затрат, и тем самым занижения налогооблагаемой базы. Распределение затрат как по видам продукции, так и по видам самих затрат производится организацией самостоятельно на основе закрепленных способов. Грамотность определения этих способов также является объектом проверки со стороны контролирующих органов. Важно не просто использовать те или иные нормы законодательства, а правильно их интерпретировать. The relevance of the problem of competent cost accounting in the organization is explained by the periodic increase in fines for incorrect calculation of taxes and fees or their late payment. Costs affect the determination of the taxable base for corporate income tax, and it is one of the most important in the Russian Federation and large in size. The largest number of violations registered by the tax authorities relate specifically to thecalculation of corporate income tax and precisely in terms of overestimating costs, and thereby underestimatingthe taxable base. The distribution of costs both by type of product and by type of costs themselves is carried out by the organization independently on the basis of fixed methods. The literacy of the definition of these methods isalso the object of verification by the regulatory authorities. It is important not only to use certain norms of legislation, but to interpret them correctly.

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)0.007
metaresearch head score (Gemma)0.029
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: none
Teacher disagreement score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0020.001
Scholarly communication0.0070.004
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.007

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 head0.028
GPT teacher head0.236
Teacher spread0.209 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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