Book Review: Guide to Health Impact Assessment (HIA) in petroleum industry
Bibliographic record
Abstract
Rapid growing industry in combination with physical, social and environmental life has raised concerns about social and environmental consequences of different industries. Therfore, Social Impact Assessment (SIA) and Environmental Impact Assessment (EIA) are included in health projects, policies and programs. HIA has been implemented in industrial projects since 1990. Several countries incuding European countries, Canada, Nezealand, USA had great developments regarding HIA in their industries. Moreover, World Health Organization has played an important role in developing HIA policy in industries. Different processes and procedures are applied for HIA but there are common points. Some of these points are as follows: -Focusing on community policies and health consequences throughout the public -Structurized format based on a health model Different HIA’s with different standards could be developed considering their common elements. It must be noted that HIA is not a part of EIA. EIA’s nature, procedures and methodology is different from HIA. However, as EIA focuses in some parts on public health, HIA could be included in EIA partially. Furhurmore, Local community around the industry is considered as HIA target point. So HIA’s essentials are considered to be beyond industrial medicine. Knowledge based economy requirements for successful technology and economy in petroleum industry are consisted of healthy workforce and community. These requirements are going to become a belief in petroleum industry because health issues guarantee economical and social developments in future. The book Reviews HIA in petroleum industry, health consequences of this industry on workforce and community. It takes the reader through a structural format towards multiple steps of HIA and enables him to have an integrated view. Establishment, development and implementation of such processes in petroleum industry will ensure economical and also health future of the workforce and public.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.087 | 0.095 |
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".