MétaCan
Menu
Back to cohort
Record W1893204210 · doi:10.1196/annals.1371.072

Asbestos Ban in India

2006· article· en· W1893204210 on OpenAlexaboutno aff
Tushar Kant Joshi, UTTPAL B. BHUVA, PRIYANKA KATOCH

Bibliographic record

VenueAnnals of the New York Academy of Sciences · 2006
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAsbestosMesotheliomaLegislationEnvironmental healthOccupational hygieneWorkforceBusinessMedicineAgricultural economicsEconomic growthOccupational safety and healthPolitical scienceLawEconomics

Abstract

fetched live from OpenAlex

Rapidly industrializing India is described by the International Monetary Fund as a young, disciplined, and vibrant economy with a projected growth of 6.7% for 2005. The total workforce of 397 million has only 7% of workers employed in the organized sector with construction, where asbestos exposure is prevalent, employing 4.4%. The domestic production of asbestos declined from 20,111 tons in 1998-1999 to 14,340 tons in 2002-2003. The imports from Russia and Canada increased from 61,474 tons in 1997-1998 to 97,884 tons in 2001-2002. The production of asbestos cement products went up from 0.68 million tons in 1993-1994 to 1.38 million tons in 2002-2003. The asbestos industry has been delicensed since March 2003. The number of asbestos-based units stood at 32, with the western state of Maharashtra having the largest number. According to official figures, the industry employs 8000 workers. The occupational exposure standard is still 2 fibers/mL, worse still, mesothelioma is not recognized as an occupational disease. The latest cancer registry data have no information on mesothelioma. The health and safety legislation does not cover 93% of workers in the unorganized sector where asbestos exposures are extremely high. Workers remain uninformed and untrained in dealing with asbestos exposure. Enforcement agencies are not fully conscious of the risks of asbestos exposure. Industrial hygiene assessment is seldom carried out and pathologists do not receive training in identifying mesothelioma histopathologically. The lack of political will and powerful influence of the asbestos industry are pushing India toward a disaster of unimaginable proportion.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.157

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.054
GPT teacher head0.327
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueAnnals of the New York Academy of SciencesSame topicOccupational and environmental lung diseasesFrench-language works237,207