The Possibility of Application of the Audit Standard 1010 and the Relevant Standards and Guidelines about Its Application in Jordan
Bibliographic record
Abstract
There wasn’t previously interested in environmental issues as in these days, the reason of what companies fouling , and affiliated factories of remnants contribute significantly to the question of environmental chaos, and these remnants through a black mass overshadowed on the three levels of life, the core of the earth and what is owned of water as strategically store, the surface of the earth and all the creatures in addition to the human, the atmosphere which is the surface of our cosmic village, as a result of this situation the problem is exacerbated, and take a new dimension, and stages, it has become necessary that the global actors stand in front of this persist on our planet. The Standard ISA 1010 came in order to be one of the most important methods that are working to reduce some cases, stop this going too far on the environmental by companies and its affiliates factories. Where this standard checksum working together to raise the auditors, and opened the way for them, to understand the size and the large responsibility placed on their shoulders towards environmental issues and matters relating to health problems associated with them. International Standard on Auditing in 1010 is not binding law, and not a system necessary to apply sharply and tough. But it is a bout bell ringing continuously in ear, heart and mind, the owner of origin, the factory Manager, administrators and auditors, that environmental damage is a collective responsibility, must commit with because when you bounce the side effects of the abuse of environmental issues we are the first to suffer them.
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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 | 0.048 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".