Example

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Currently the comparison between the work and the reliability of a rescue dog unit is based totally upon subjective assessment and by sight evaluation of the work itself, or from the association exams which the unit belongs to: competitions such as I.R.O., I.P.O., E.N.C.I., Regional competitions or various national and local associations competitions.  Which ones should be regarded for their reliability in measuring performance in a surface search for a missing person?

Currently the comparison between the work and the reliability of a rescue dog unit is based totally upon subjective assessment and by sight evaluation of the work itself, or from the association exams which the unit belongs to: competitions such as I.R.O., I.P.O., E.N.C.I., Regional competitions or various national and local associations competitions.  Which ones should be regarded for their reliability in measuring performance in a surface search for a missing person?

If the objective is to cover the whole area and to do a clearance there are some issues that need to be solved:

• Did the dog cover the whole area? (for example, a dog that not under control of the handler)
• Does the handler follow the strategy given at the beginning?
• Is the dog at the proper distance in relation to the handler?
• Does the dog look for the tracks of the figure or is it working by scent ?
• Has the wind direction been taken into consideration?
• and many more...

Example 1 (Pic.1)

• finding the figure as soon as the dog intersects a trail
• Zona non coperta totalmente Area not totally covered

Example 2 (Pic.2)

• the dog less and less goes away form the handler
• Area not totally covered

Example 3 (Pic.3)

• Correct tactics and distances
• area covered

In this case the application and usage of the GAUCS (licensed in Italy in 2011, eurepean patente pending) methodology, helps us by giving us a whole series of objective data sources that allow us to better evaluate the reliability of the work done in the field by the canine units. 

Moreover it can eventually be used in working exams adding objective data that the judges  dont normally see in tests (wrong area, dog that dosen’t go too far, area coverage, …) 

Also, you can get all the accessory data to improve the performance of the work, analyzing the graphs in the Cloud application that records the data.

(Pic.4)