HIERARCHAL INDUCTIVE PROCESS MODELING AND ANALYSIS ...

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• For an reMSE cutoff of less than 0.4 the Median Activation Values for all 5 entities blend together and are not useful. However, for reMSE cutoff equal to 0.4 or greater the Median Activation Values seem to indicate that zooplankton yields the most discriminatory powers which could be due to the numerous experiments for which we obtain zero good fit models. These zeros could be the result of two things, either that the discriminatory power of Zooplankton is superior or the most plausible answer at this time would be that zooplankton are not appropriately defined in the process library. That being said there are a couple of elements that raise question in regards to the accuracy of the model selection process or the process-entity library. These elements being: • The behavior observed in Figure 7 with time-series for four or five of the entities as an average number of good fit models very close or equal to zero as well as a spike in reMSE fosters doubt as to the accuracy of the selection process when provided too many constraints. • The lack of good fit models, under reMSE cutoffs ranging from 0.1 to 1, for many of the experiments that included Zooplankton time-series constraints. This leads us to conjecture that when HIPM is given more than 3 data-series the system becomes overconstrained thus preventing it from accurately selecting models. Another conjecture is that the entity Zooplankton as defined in the process-entity library needs to be reviewed; it could be this element alone that is at the origin of this issue in the model selection. More specifically, one of the assumption of the system is that zooplankton feed very little if at all on Phaeocystis antartica as they are more resistant to grazing in comparison to diatoms that are more typically grazed upon by zooplankton. The way the process-library is currently set-up diatoms are 31
not taken into consideration which could then in turn affect how well zooplankton performs when fitting models. 32
Page 1 and 2: HIERARCHAL INDUCTIVE PROCESS MODELI
Page 3 and 4: APPENDIX . . . . . . . . . . . . .
Page 5 and 6: DEDICATION This Thesis is dedicated
Page 7 and 8: LIST OF TABLES 1 Example of entity
Page 9 and 10: LIST OF SYMBOLS P = Amount of Phyto
Page 11 and 12: models of natural systems are non-u
Page 13 and 14: Figure 1: This schematic represent
Page 15 and 16: specifying the space of possible eq
Page 17 and 18: phytoplankton productivity and comm
Page 19 and 20: 2 METHOD The method employed in thi
Page 21 and 22: set of values that respect the cons
Page 23 and 24: fitted such as for “conc”, with
Page 25 and 26: Table 2: Defining a process - Growt
Page 27 and 28: upon whether certain time-series ar
Page 29 and 30: 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: experiment 22, yielding no good fit
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 and 82: APPENDIX A. Sample CIAO data - 1997
Page 83 and 84: "death_rate":0.02, "respiration_rat
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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