I think that the problem is that most species descriptions are written a way that person1 interprets specimenA as conceptB and person2 interprets specimenA and ConceptC.<div><br></div><div>This needs to be made more scientific so that one can test what proportions of specimens actually conform to the description (concept).</div>
<div><br></div><div>These descriptions should be open, world readable and reference-able via a URI.</div><div><br></div><div>Respectfully,</div><div><br></div><div>- Pete</div><div><br></div><div>** There also seems to be mismatch between the concept the human identifier choose (often via a key) and the species description (concept) to which you are saying their data applies.</div>
<div><br></div><div><br><div class="gmail_quote">On Sat, Jun 12, 2010 at 7:50 PM, Richard Pyle <span dir="ltr"><<a href="mailto:deepreef@bishopmuseum.org">deepreef@bishopmuseum.org</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
<div class="im"><br>
> That said modeling relationships between taxonomic publications where<br>
> the authors actually read the original species description, reviewed<br>
> the type specimens, and thought about the actual species conscription is<br>
appropriate.<br>
<br>
</div><div class="im">This is the sort of things the Meta-Authorities would take into account when<br>
selecting a "follow-this-treatment" Usage-Instance for the preferred<br>
treatment of a name.<br>
<br>
<br>
</div><div class="im">> Also consider that a large proportion of specimens are misidentified,<br>
> and it occurs to me that modeling things like species occurrences as<br>
> if they are Puma concolor (Linnaeus, 1771) sensu stricto is probably<br>
> not appropriate. At best they are something like (Felis concolor /<br>
> Puma concolor) with some significant level of error.<br>
<br>
</div><div class="im">GNA can't helpw ith that directly -- but it can help indirectly. Imagine a<br>
service that takes ever specimen in a given collection's database, and runs<br>
it against a mapping service as I described in the previous message. I can<br>
easily imagine a GIS-based algorithm that finds "outliers" -- that is<br>
occurrence records that appear to be outside the distribution based on the<br>
occurrence records from other sources. A clver/robust such algorithm could<br>
probably even discern whether the outlier likely represented a range<br>
extension (e.g. poorly-known species, plausible extansion), vs. a<br>
misidentification (e.g., well-known species and/or common<br>
misidentification).<br>
<br>
This would lead to a set of flagged records from the collection that might<br>
be misidentified.<br>
<br>
Rich<br>
<br>
<br>
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</div></div></blockquote></div><br><br clear="all"><br>-- <br>----------------------------------------------------------------<br>Pete DeVries<br>Department of Entomology<br>University of Wisconsin - Madison<br>445 Russell Laboratories<br>
1630 Linden Drive<br>Madison, WI 53706<br>GeoSpecies Knowledge Base <br>About the GeoSpecies Knowledge Base<br>------------------------------------------------------------<br>
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