Bibliographic record
Abstract
XBRL (eXtensible Business Reporting Language) as an application of XML (eXtensible Markup Language) technology in the field of business reporting, uses information technology to add tags for the financial reporting metadata, to achieve unstructured data processing effectively. At first, this paper analyzes the XBRL technology framework, finds out the concepts and relationships between them, and accordingly designs the XBRL domain ontology model, then based on the model we have proposed, we choose OWL DL ontology description language and Protégé3.4, take balance sheet as an example, and apply the XBRL ontology model to balance sheet to construct the taxonomy and instance, and thereby achieve the implementation of XBRL domain ontology construction. XBRL needs the integration of the accounting, engineering, computer, management and other disciplines, and the future research should be committed to solve the lack of semantic and reasoning mechanism to implement the further promotion and application of XBRL financial reporting.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.020 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".