Auditing Estimates in Financial Statements: A Case Study of a Fish Farm's Biological Asset
Bibliographic record
Abstract
Abstract Recent decades have witnessed an increase in the overall uncertainty inherent in financial statements. It is now common for the financial statements of a public company to include estimates with measurement uncertainties that exceed materiality. As a result, auditing students are now required to have a more profound understanding of (i) the impact of accounting estimates on risk assessment and (ii) the development of audit procedures to deal with accounting estimates. This case allows students to explore CAS 540 – Accounting Estimates by assuming the role of Atlantic Canada Aquaculture's (ACA's) auditor. ACA operates in Eastern Canada and is prohibited by Canadian regulations to catch and release the fish in their farm to determine their biological asset value. As a result, ACA developed a statistical model to determine the number and weight of the fish in their farm. The model is based on various estimates, such as water temperature, survival rates, and food quality. Students are required to explore the impacts of the model's estimates on the inherent risks in the financial statements and assess the reasonability of the model from an external auditor's perspective. The case also allows students to explore CAS 620 – Using the work of an Auditor's Expert and CAS 701 – Communicating Key Audit Matters in the Independent Auditor's Report in the context of the audit of biological assets.
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 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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".