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| 02279cam 2200397zi 4500 |
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001 | 9.872098 |
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003 | CaOODSP |
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005 | 20221107163548 |
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006 | m o d f |
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007 | cr bn||||||||| |
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008 | 190426s1999 onca ob f000 0 eng d |
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040 | |aCaOODSP|beng|erda|cCaOODSP |
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041 | |aeng|bfre |
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043 | |an-cn--- |
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086 | 1 |aEn13-5/99-1E-PDF |
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100 | 1 |aKaiser, Klaus L. E., |d1941- |eauthor. |
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245 | 10|aProbabilistic neural network (PNN) methodology for the prediction of acute toxicity of chemicals to fathead minnow based solely on chemical structure-derived input parameters / |cKlaus L.E. Kaiser, Ph.D., and Stefan P. Niculescu, Ph.D., National Water Research Institute. |
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264 | 1|aBurlington, Ontario : |bNational Water Research Institute = Institut national de recherche sur les eaux, |c1999. |
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300 | |a1 online resource (37 pages) : |billustrations. |
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336 | |atext|btxt|2rdacontent |
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337 | |acomputer|bc|2rdamedia |
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338 | |aonline resource|bcr|2rdacarrier |
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490 | 1 |aNWRI contribution ; |vno. AEP-TN99-001 |
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500 | |a"In partial fulfillment of contract KW405-8-0512. Submitted to CCEB, 10 Dec. 1998, revised, 8 Jan. 1999." |
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500 | |aDigitized edition from print [produced by Environment and Climate Change Canada]. |
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504 | |aIncludes bibliographical references. |
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520 | 3 |a"This report gives an overview of the probabilistic neural network methodology, the individual results and a brief comparison with other available toxicity estimation models for the prediction of 96-hr LC50 values for the fathead minnow (Pimephales promelas) based on an 865 chemicals data set. It has been prepared as a background report for the Commercial Chemicals Evaluation Branch, Environment Canada, Ottawa"--Abstract, page 3. |
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546 | |3Includes management perspective and abstract in French. |
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692 | 07|2gccst|aToxicology |
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692 | 07|2gccst|aFreshwater fish |
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700 | 1 |aNiculescu, Stefan P., |eauthor. |
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710 | 1 |aCanada. |bEnvironment Canada. |
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710 | 2 |aNational Water Research Institute (Canada) |
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830 | #0|aNWRI contribution ;|vno. AEP-TN99-001.|w(CaOODSP)9.844121 |
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856 | 40|qPDF|s1.22 MB|uhttps://publications.gc.ca/collections/collection_2019/eccc/en13-5/En13-5-99-1-eng.pdf |
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