LEADER 02934nam 2200769Ia 450 001 9911004890403321 005 20251116174352.0 010 $a9786611905224 010 $a9781281905222 010 $a1281905224 010 $a9781933995892 010 $a1933995890 010 $a9781597340359 010 $a1597340359 035 $a(CKB)1000000000335992 035 $a(EBL)686358 035 $a(OCoLC)610027127 035 $a(SSID)ssj0000291311 035 $a(PQKBManifestationID)11219619 035 $a(PQKBTitleCode)TC0000291311 035 $a(PQKBWorkID)10249052 035 $a(PQKB)10109719 035 $a(Au-PeEL)EBL231583 035 $a(CaPaEBR)ebr10379717 035 $a(OCoLC)475937481 035 $a(Perlego)532552 035 $a(MiAaPQ)EBC231583 035 $a(MiAaPQ)EBC686358 035 $a(Au-PeEL)EBL686358 035 $a(EXLCZ)991000000000335992 100 $a19990405d1999 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aChemicals, cancer, and choices $erisk reduction through markets /$fby Peter VanDoren 205 $a1st ed. 210 $aWashington, D.C. $cCato Institute$dc1999 215 $a1 online resource (110 p.) 300 $aDescription based upon print version of record. 311 08$a9781882577798 311 08$a1882577795 311 08$a9781882577781 311 08$a1882577787 320 $aIncludes bibliographical references (p. 85-93) and index. 327 $aChemicals, Cancer and Choices: Risk Reduction Through Markets; Copyright; Table of Contents; Acknowledgments; 1. Introduction; 2. Effects of Synthetic Chemical Exposures on Human Health; 3. Managing Private Risks from Chemical Exposures; 4. Managing Public Risks from Chemical Exposures; 5. Insurance; 6. What Should Be Done?; NOTES; REFERENCES; INDEX; CATO; Back Cover 330 $aOne of the most important policy questions we face concerns the health effects on humans of environmental chemicals. The author debunks the conventional wisdom that all animal studies measuring chemical exposure can be used to evaluate the actual danger to people. 606 $aHealth risk assessment 606 $aHealth risk assessment$xGovernment policy$zUnited States 606 $aHealth risk assessment$xEconomic aspects$zUnited States 606 $aFree enterprise$zUnited States 606 $aEnvironmental toxicology 615 0$aHealth risk assessment. 615 0$aHealth risk assessment$xGovernment policy 615 0$aHealth risk assessment$xEconomic aspects 615 0$aFree enterprise 615 0$aEnvironmental toxicology. 676 $a615.9/02 700 $aVanDoren$b Peter M$01822027 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911004890403321 996 $aChemicals, cancer, and choices$94388066 997 $aUNINA LEADER 03215nam 22005775 450 001 996641269503316 005 20250626164030.0 010 $a9783031770661 010 $a3031770668 024 7 $a10.1007/978-3-031-77066-1 035 $a(CKB)37122156900041 035 $a(MiAaPQ)EBC31868085 035 $a(Au-PeEL)EBL31868085 035 $a(DE-He213)978-3-031-77066-1 035 $a(OCoLC)1490380309 035 $a(EXLCZ)9937122156900041 100 $a20250101d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Analytics and Learning on Temporal Data $e9th ECML PKDD Workshop, AALTD 2024, Vilnius, Lithuania, September 9?13, 2024, Revised Selected Papers /$fedited by Vincent Lemaire, Georgiana Ifrim, Anthony Bagnall, Thomas Guyet, Simon Malinowski, Patrick Schäfer, Romain Tavenard 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (156 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v15433 311 08$a9783031770654 311 08$a303177065X 327 $aConformal Prediction Techniques for Electricity Price Forecasting -- Multivariate Human Activity Segmentation Systematic Benchmark with ClaSP -- Comparing the Performance of Recurrent Neural Network and Some Well Known Statistical Methods in the Case of Missing Multivariate Time Series Data -- Accurate and Efficient Real World Fall Detection Using Time Series Techniques -- Highly Scalable Time Series Classification for Very Large Datasets -- Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble An Improved ROCKET Algorithm for Multivariate Time Series Analysis -- Change Detection in Multivariate data streams Online Analysis with Kernel QuantTree -- Weighted Average of Human Motion Sequences for Improving Rehabilitation Assessment. 330 $aThis book constitutes the refereed proceedings of the 9th ECML PKDD workshop on Advanced Analytics and Learning on Temporal Data, AALTD 2024, held in Vilnius, Lithuania, during September 9-13, 2024. The 8 full papers presented here were carefully reviewed and selected from 15 submissions. The papers focus on recent advances in Temporal Data Analysis, Metric Learning, Representation Learning, Unsupervised Feature Extraction, Clustering, and Classification. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v15433 606 $aArtificial intelligence 606 $aArtificial Intelligence 615 0$aArtificial intelligence. 615 14$aArtificial Intelligence. 676 $a006.3 700 $aLemaire$b Vincent$01460554 701 $aIfrim$b Georgiana$01460549 701 $aBagnall$b Anthony$01460551 701 $aGuyet$b Thomas$01380137 701 $aMalinowski$b Simon$01460553 701 $aSchäfer$b Patrick$01783566 701 $aTavenard$b Romain$01460550 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996641269503316 996 $aAdvanced Analytics and Learning on Temporal Data$94311425 997 $aUNISA