Form Conditions of Check Issuing in Iranian and British Law
Bibliographic record
Abstract
The aim of this research is to evaluate the components and to state the form conditions of check issuing in statute of two countries of Iran and England. Due to the wide use of check and prevalence of its use as a payment instrument, and even the instrument for obtaining reputation such as promissory note and draft, the issuer should be familiar with the form conditions of check issuing to prevent possible problems caused by lack of knowledge. Since today these documents play a major role in communicating and business transactions, and strength most of deals. This led to prevalence of using aforementioned documents in establishment of trade exchanges, so that it can be said that these documents are an inseparable part of business transactions. The results obtained from the other researches and studies indicate that form conditions of check issuing are almost identical in Iranian and British Laws, that observing some of them is necessary, and if they are not observed, commercial document of the check will not have the necessary validity. Of mandatory form conditions include the date of issuing check include: Check issuing date, mentioning the word of check, unconditional order of payment of the amount check, check amount, mentioning the name of drawee on the check, mentioning the name of Mohil, indicating the place of check issuing, indicating the name of the holder, signature of check . In this study form conditions of issuing electronic check have also been investigated.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".