HIERARCHAL INDUCTIVE PROCESS MODELING AND ANALYSIS ...

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B. Full entity Specification File #!/usr/bin/python """ This is the revised file for entity specification Stuart Borrett April 26, 2007 """ from ross_lib import *; # import library # observed primary producer p1 = entity_instance(pe, "phyto", {"conc": ("system", "PHA_c", (0,600)), # ugC/L "growth_rate": ("system", 0, (0,1)), "growth_lim": ("system", 1, (0,1))}, {"max_growth":0.59, "exude_rate":0.19, "death_rate":0.025, "Ek_max":30, "biomin":0.025, "PhotoInhib":200} ); # unobserved grazer with initial value from [0,1] default 0.1 Z1 = entity_instance(ze, "zoo", {"conc": ("system", 0.1 , (0.10,510)), "growth_rate": ("system", 0.1, (0, 1))}, {"assim_eff":0.75, 73
"death_rate":0.02, "respiration_rate":0.019, "gmax":0.4, "gcap":200} ); # observed nitrate no3 = entity_instance(no3, "NO3", {"conc": ("system", "NITR", (0,32)), "mixing_rate": ("system", 0, (0,1))}, None); # unobserved iron fe = entity_instance(fe, "Fe", {"conc": ("system",.00042920, (0,0.001)), "mixing_rate": ("system", 0, (0,1))}, None); # observed/exogenous ENVIRONMENT e1 = entity_instance(ee, "environment", {"PUR": ("exogenous", "PURL", None), "TH2O": ("exogenous", "TEMP", None), "ice":("exogenous", "AI", None) }, {"beta":0.7} ); # unobsevable detritus with initial value from [0,1] default 0.1 D1 = entity_instance(de, "detritus", {"conc": ("system", 0.1, (0.001, 210))}, None); 74
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HIERARCHAL INDUCTIVE PROCESS MODELI
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APPENDIX . . . . . . . . . . . . .
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DEDICATION This Thesis is dedicated
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LIST OF TABLES 1 Example of entity
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LIST OF SYMBOLS P = Amount of Phyto
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models of natural systems are non-u
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Figure 1: This schematic represent
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specifying the space of possible eq
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phytoplankton productivity and comm
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2 METHOD The method employed in thi
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set of values that respect the cons
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fitted such as for “conc”, with
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Table 2: Defining a process - Growt
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upon whether certain time-series ar
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3 COMPUTATIONAL RESULTS The main to
Page 31 and 32: ● ● ● 2.0 ● [D,N] ●●●
Page 33 and 34: 3.1 Increase in number of time-seri
Page 35 and 36: Table 4: This table represents each
Page 37 and 38: different restriction powers based
Page 39 and 40: experiment 22, yielding no good fit
Page 41 and 42: not taken into consideration which
Page 43 and 44: Table 5: This table summarizes all
Page 45 and 46: Table 6: This table summarizes all
Page 47 and 48: 4.2 Preliminaries Both Models A and
Page 49 and 50: In addition to these 2 Lemmas, let
Page 51 and 52: When t → ∞ we get the following
Page 53 and 54: For the Zooplankton not to go to ze
Page 55 and 56: ( ∴ P 0 + a ) 13 α − δ Z 0 e
Page 57 and 58: 4.4 Model B Shifting our focus to M
Page 59 and 60: Using the exogenous variables time-
Page 61 and 62: Then finding an lower bound, dD l d
Page 63 and 64: Thus, using Proposition 1 we get, d
Page 65 and 66: ∴ a 23 = 4.5.10 −4 ≤ F (t)
Page 67 and 68: Model C Where, dP dt dZ dt dD dt dN
Page 69 and 70: Next we turn our attention to P (t)
Page 71 and 72: We can rewrite D u (t) as, D u (t)
Page 73 and 74: his research on the Ross Sea Phytop
Page 75 and 76: zooplankton to be properly fitted m
Page 77 and 78: eally well (Atanasova et al. 2007).
Page 79 and 80: [9] Dzeroski, S. and Todorovski, L.
Page 81: APPENDIX A. Sample CIAO data - 1997
Page 85 and 86: "death_rate": (0.02,0.04), "Ek_max"
Page 87 and 88: # --- GROWTH --- lib.add_generic_pr
Page 89 and 90: "arrigoetal1998", "light_lim", [("P
Page 91 and 92: lib.add_generic_process( "death_exp
Page 93 and 94: lib.add_generic_process( "holling_t
Page 95 and 96: [], {"max_mixing_rate":(0.000001,1)
Page 97 and 98: Model E [ dP [ ] dt = (1 − E ice
Page 99 and 100: Model G [ dP [ ] ( dt = (1 − E ic
Page 101: BIOGRAPHICAL SKETCH I was born and
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