### Table 5 Misclassified samples from the 1993 data set with the correspond- ing values assigned by the PLS models constructed with the data from 1992 and the data from 1993 (their own model), both of them only with the CieLab variables

"... In PAGE 4: ... To evaluate the future prediction capacity of the model the following process was observed: (ij Apply the 1992 model to the 1993 data set and compare the result with the colour testing of 1993; and (ii) compare these results with what the 1993 PLS model would have obtained, assuming this is the model best adapted to explain the 1993 harvest. Table5 shows the results. Only eight samples out of 170 were misclassified by the 1992 PLS model when predicting the 1993 data.... ..."

### Table 3 Assigning a Variable Subindex Based on Direct Measure or Indicators

"... In PAGE 44: ... Indicators are easily observed or measured characteristics that are correlated with a quantitative measure of a variable. For example, a subindex could be assigned to the frequency of overbank flow variable based on indicators such as the presence of certain vascular or nonvascular plant species or evidence of recent flooding such as water marks or wrack ( Table3 ). Table 3 Assigning a Variable Subindex Based on Direct Measure or Indicators... ..."

### Table 1. Assignment of Variables.

2002

Cited by 2

### Table 1. Symbolic Interpretation of Reachability Logic To read the rules of Table 1 some notation needs to be explained. For D a constraint system and r a set of variables (to be reset) r(D) denotes the set of variable assignments fr(v) j v 2 Dg. Now D quot; denotes the following set of variable assignments D quot; = fw j 9v 2 D9d MD(l; v):w = v dg An important observation is that, whenever D is a constraint system (i.e. a con- junction of atomic clock and data constraints), then so are both r(D) and D quot;. 9

in Copyright C

"... In PAGE 12: ...directed graphs (with clock and data variables as nodes), these operations as well as testing for inclusion between constraint systems may be e ectively im- plemented in O(n2) and O(n3) using shortest path algorithms [11, 12, 6]. Now, by applying the proof rules of Table1 in a goal directed manner we obtain an algorithm (see also [13]) for deciding whether a given symbolic network con guration [l; D] satis es a property 93 . To ensure termination (and e ciency), we maintain a (past{) list L of the symbolic network con gurations encountered.... ..."

### Table 1. Symbolic Interpretation of Reachability Logic To read the rules of Table 1 some notation needs to be explained. For D a constraint system and r a set of variables (to be reset) r(D) denotes the set of variable assignments fr(v) j v 2 Dg. Now D quot; denotes the following set of variable assignments D quot; = fw j 9v 2 D9d MD(l; v):w = v dg An important observation is that, whenever D is a constraint system (i.e. a con- junction of atomic clock and data constraints), then so are both r(D) and D quot;.

"... In PAGE 10: ...directed graphs (with clock and data variables as nodes), these operations as well as testing for inclusion between constraint systems may be e ectively im- plemented in O(n2) and O(n3) using shortest path algorithms [11, 12, 6]. Now, by applying the proof rules of Table1 in a goal directed manner we obtain an algorithm (see also [13]) for deciding whether a given symbolic network con guration [l; D] satis es a property 93 . To ensure termination (and e ciency), we maintain a (past{) list L of the symbolic network con gurations encountered.... ..."

### Table 1. Rules defining term assignment judgements of .

1996

"... In PAGE 4: ...de ned as follows: s := x variable j xA:s -abstraction j st application j [ A]s named-term j B:s -abstraction. The rules of term assignment judgements are dis- played in Table1 . Observe the symmetry between the structural rules for -contexts and those for -contexts.... ..."

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### Table 2: Description of Variables

2003

"... In PAGE 20: ...Table2 provides a summary of the control variables and their descriptive statistics. 5.... ..."

### Table 2: Observable variables

"... In PAGE 6: ... Communication between the buying services and the auction house takes place via channels. We define observable variables for the auction service in 2 n winBid 0 1 ask winBid = max(bid1,bid2,bid5) bid5 bid2 bid1 Figure 6: Observable States of the Auction House Table2 below. Table 2: Observable variables... ..."

### Table 1: Means (Standard Deviations) of Key Variables

1999

Cited by 3