1. automated detection errors validation

2. Is your project feasible?
Non-automated analysis is too costly, and too inconsistent, so we need automation with:
Level of automated false positives << difference being tested
e.g. if you are comparing use of sites, the error level needs to be
smaller than the larger of sampling error or the differences found.
e.g. if you want to detect a population trend of 5% in 1 year the error level needs to be less than say 1%.
The aim:
Validation becomes quality control i.e. ‘meets the standard’ rather than editing the data.
Errors are not removed.
A pilot study may be needed in difficult places - this was done by Jay Barlow et al for the Vaquita monitoring and a power analysis was then used to produce a spatial design. The performance prediction proved accurate.

3. What kind of thing is train detection?
Equations. Always published
Pure maths – exact, theoretical.
Applied maths – introduces approximations.
Statistics – deals with extrapolation from samples. Introduce sampling error.
Multivariate and clustering methods. Introduce classifiers.
Pattern recognition. Handles ‘polluted’ data - often composed mainly of data that do not belong to the focal subject(s) ...face recognition, train detection, etc
Intelligent recognition. Uses a wide and adaptable conceptual structure of classifiers.
Computer code. Rarely published. 'Black boxes' by virtue of complexity, and unknown input.
Machine learning, neural networks, and 'hand-crafted' algorithms are generally in (5)
Humans, as detectors, are a long way ahead at (6)

4. Human visual validation can improve on the KERNO classifiers …
…because a human observer takes a wider temporal view of the data and recognises patterns such as:
Recurring inter-click-intervals from boat sonar.
‘Chance trains’ resembling ambient noise.
Unusual click rate profiles with no ‘normal’ porpoise data - weak unknown train sources.
Improving automated rejection of these errors often comes at a cost: wrongly excluding some true positives.

5. Encounter classifiers:
A third level of classifier.
1. Click classification
2. Train classification
3. Encounter classification
Encounter classifiers look at a wider span of data than the train classifier and see the character of a whole encounter.
The can be designed for a specific task in a specific location – e.g. the Hel1 classifier was developed for the Baltic Sea with the help of a large bowl of strawberries.
Three new sources of false positives were found: mini-bursts, chink spikes, and replays.
It has been validated on the whole, huge, SAMBAH data set.

6. Test case: SAMBAH - a very low density population
1,343 data files
61% no detections
15% have 1 to 20 DPM (detection positive minutes)
False positives:
76 - boat sonars
65 - playbacks
10 - WUTS
5 - minibursts or chink spikes
2 - porpoises!
157 of 176,000 DPM were false = 0.1% = < 1 error / year

7. SAMBAH: should we remove the errors?
… not needed for population estimation as the rate is far below other sources of error,
but a few % of files with low positive rates had only false positives, and removing those improves the presence/absence distribution map.

8. Q classes: Hi, Mod,Lo, ? represent the algorithm’s confidence that it was from a train source – cetacean, boat sonar, or WUTS, and not just a ‘chance train’.
Normally we use Q classes Hi and Mod
Species classes: NBHF, other cet, sonar, unclassed.
Q and species are more or less independent.

9. Validating exercises

10. Workshop Files 1.CPOD1372_teste_da_gaiola
Guiana Dolphins and Fransicana
Renan Paitach
Good data
2. Gulf of Alaska WUTS
WUTS
Kate Stafford
Bad classification
3. Kawda, Sarjekot
Humpback Dolphins and Finless Porpoise
Ketki Jog
4. Kawda Finless bad
5. Kawda One species or two
6. Some errors
Sonars and Irawaddy River Dolphins
Danielle Kreb
1. Should an encounter classifier be used?
2. This is how bad they get
3. Find some errors
4. Examples of errors
5. Check species classification
6. Find first correct dolphin. This is a compilation of errors.
A coal barge, file 6