You are a useful assistant that extracts tuples, that we call CLAIM, each contains information of only one cell from an HTML table that will be provided below.
The format to extract a CLAIM is:
<{<name, value>, <name, value>, … }>, <MEASURE, value>, <OUTCOME, value>
The format must be exactly this one and only has data from one cell of the html table.
Where each <name, value> in the vector will determine the position of the cell.
The vector may contain more <name, value> because some cells in the table are not scientific results, but just features, therefore they will not be used as measure and outcome, but only as part of the vector, an example can be the number of patients in an experiment.
Make sure to replicate exactly how the vector is made above.
Where <MEASURE, value> is the value of the MEASURE used in that cell.
A MEASURE is something that can be considered a scientific measure used to get scientific results, for example the number of patients is NOT a measure.
Where <OUTCOME, value> is the actual value, usually a number in the cell.

It is crucial to understand whether something is a real MEASURE or not, in the case of not being a MEASURE you must put that cell in the vector that determines the position.
This means not necesarily every cell equals one CLAIM, make sure to understand that mere features and characteristics are not measures so never include them in the MEASURE, only in the vector.
A MEASURE may not be mentioned in the table but may be understood by context and data, for example a percentage, but it’s never just a number, it’s a scientific measure, not a feature.
Extract from the table all the information, analyze data, decide whether it’s a measure or not (a mere number of patients, number of experiments, numbers which are not related to a scientific measure are never MEASURE), then create the CLAIM, in most cases you won’t have one claim per each cell, but less.
Per each claim you have to decide which MEASURE is used, it’s important that each claim has a MEASURE, if there is one used on that cell, be careful as the measure is the most important part of the claim.
Don’t show the whole process of analyzing, just show the final results with the CLAIM produced, following the format precisely.

Here is the HTML Table:
<html>
<head>
<meta charset=“utf-8”/>
<style>
table, th, td {
border: 1px solid black;
font-size: 10px;
}
</style>
</head>
<body>
<table frame=“hsides” rules=“groups” width=“100%”>
<thead>
<tr>
<td>
<b>
Number
</b>
</td>
<td>
<b>
CS
</b>
</td>
<td>
<b>
US
</b>
</td>
<td>
<b>
Magnitude
</b>
</td>
<td>
<b>
Interval (s)
</b>
</td>
</tr>
</thead>
<tbody>
<tr>
<td>
1
</td>
<td>
[1, 0, 0]
</td>
<td>
[1, 1, 0, 0]
</td>
<td>
0.8
</td>
<td>
2
</td>
</tr>
<tr>
<td>
2
</td>
<td>
[0, 1, 0]
</td>
<td>
[1, 0, 1, 0]
</td>
<td>
0.5
</td>
<td>
3
</td>
</tr>
<tr>
<td>
3
</td>
<td>
[0, 0, 1]
</td>
<td>
[1, 0, 1, 1]
</td>
<td>
1.0
</td>
<td>
4
</td>
</tr>
</tbody>
</table>
</body>
</html>


Caption provided for the table:
Definition of the inputs to the model. Each CS-US association is defined by unique CS and US vectors. During conditioning, rewards are presented with a certain magnitude, and after a certain delay after CS onset.

Paragraph quoting the table in the document:
As shown in Table 1, there is an overlap between the different tastes of the US, rendering harder the task to distinguish them. Moreover, each US representation is multiplied by a magnitude, representing the quantity of food delivered. In this article, this magnitude is the same for all tastes composing the US. A conditioning trial is composed of a first reset interval of 1 s where no input is given to the network (all elements of the CS and US representations are set to 0). At time t = 1s, the CS representation is set to the corresponding vector. This input is maintained for a given duration, whose value depend on the CSUS association (2 s for CS1-US1, 3 s for CS2-US2, 4 for CS3-US3). These different interval durations are chosen to show that the network can indeed learn different CS-US intervals without any modification, but different combinations would lead to similar results.
Most experiments in this section concern the concurrent learning
of the three CS-US associations described in Table 1.


DO NOT IGNORE THE FORMAT, YOU MUST FOLLOW IT PRECISELY.
Always remember to put in the vector anything that is not a measure and don’t ignore cells, include all cells that are just text in the vector.
If there’s a cell you don’t know where to put, don’t ignore it, insert it in the vector.
It’s fundamental that all claims will contain every piece of information in the table, DO NOT IGNORE CELLS.