000			02425cam a2200277zu 4500
001			88865105
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005			20250107231328.0
006			m o d
007			cr un
008			250108s2009 fr | o|||||0|0|||eng d
020			_a9780231132138
035			_aFRCYB88865105
040			_aFR-PaCSA _ben _c _erda
100	1		_aPilkey, Orrin H.
245	0	1	_aUseless Arithmetic _bWhy Environmental Scientists Can't Predict the Future _c['Pilkey, Orrin H.']
264		1	_bColumbia University Press _c2009
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aPilkey, Orrin H.
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88865105 _qtext/html _a
520			_aNoted coastal geologist Orrin Pilkey and environmental scientist Linda Pilkey-Jarvis show that the quantitative mathematical models policy makers and government administrators use to form environmental policies are seriously flawed. Based on unrealistic and sometimes false assumptions, these models often yield answers that support unwise policies. Writing for the general, nonmathematician reader and using examples from throughout the environmental sciences, Pilkey and Pilkey-Jarvis show how unquestioned faith in mathematical models can blind us to the hard data and sound judgment of experienced scientific fieldwork. They begin with a riveting account of the extinction of the North Atlantic cod on the Grand Banks of Canada. Next they engage in a general discussion of the limitations of many models across a broad array of crucial environmental subjects. The book offers fascinating case studies depicting how the seductiveness of quantitative models has led to unmanageable nuclear waste disposal practices, poisoned mining sites, unjustifiable faith in predicted sea level rise rates, bad predictions of future shoreline erosion rates, overoptimistic cost estimates of artificial beaches, and a host of other thorny problems. The authors demonstrate how many modelers have been reckless, employing fudge factors to assure "correct" answers and caring little if their models actually worked. A timely and urgent book written in an engaging style, Useless Arithmetic evaluates the assumptions behind models, the nature of the field data, and the dialogue between modelers and their "customers."
999			_c71370 _d71370