Application of Accounting Standards in the Valuation of Biological Assets: An Analysis of the Poultry Sector in Tungurahua, Ecuador
Bibliographic record
Abstract
This study analyzes the application of International Accounting Standard IAS 41—Agriculture in poultry companies (ISIC A0146.03) in the province of Tungurahua, Ecuador, with the aim of evaluating the degree of regulatory compliance, the level of technical knowledge of accounting managers, and the impact of the lack of homogenization in the presentation of financial statements. The research is based on a quantitative–descriptive approach, through the application of a structured questionnaire to 26 representatives of poultry companies, complemented with the financial analysis of the activity during the period 2010–2024. The results show that the application of IAS 41 is not exhaustive or uniform, and various valuation methods are used that are not always aligned with the fair value approach required by the standard. Likewise, significant heterogeneity in accounting criteria is detected, which limits comparability and reduces the usefulness of financial information for decision-making. Most respondents have general but limited knowledge of the standard, which affects its technical implementation. The study concludes that it is necessary to strengthen specialized accounting training, establish sectoral standardization criteria, and promote regulatory supervision to ensure transparent, coherent, and useful financial information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".