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67match
elainefromstow.com
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3 co-citations4 shared tech1 shared categoryenvironment
66match
feelsingapore.com
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65match
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59match
biobasemaps.com
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3 co-citations5 shared tech1 shared categoryenvironment
56match
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56match
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3 co-citations1 shared tech1 shared categoryenvironment
55match
bioboat.org
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4 co-citations4 shared tech1 shared categoryenvironment
53match
coopforest.com
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2 co-citations2 shared tech1 shared categoryenvironment
53match
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2 co-citations5 shared tech1 shared categoryenvironment
53match
fieff.com
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2 co-citations3 shared tech1 shared categoryenvironment
52match
lapblack.com
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3 co-citations4 shared tech1 shared categoryenvironment
52match
biocharireland.com
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1 co-citations3 shared tech1 shared categoryenvironment
52match
gretahart.com
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2 co-citations4 shared tech1 shared categoryenvironment
51match
arrecifenatura.com
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2 co-citations4 shared tech1 shared categoryenvironment
51match
geocoherence.com
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2 co-citations4 shared tech1 shared categoryenvironment
51match
jackiegarcia-yi.com
Jackie Garcia-Yi
1 co-citations6 shared tech1 shared categoryenvironment
50match
greenhour.com
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3 co-citations6 shared tech1 shared categoryenvironment
50match
adoptawildlifeacre.com
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3 co-citations6 shared tech1 shared categoryenvironment

How the match score works

Each match is a 0–100 similarity score — the higher it is, the more two sites resemble one another. It’s computed automatically from our own crawl data (never from what a site says about itself) by combining several independent signals, so a high score means several of them point the same way:

No single signal decides the result — they’re blended together. Treat the score as a way to rank candidates rather than an absolute percentage; the chips on each result show which signals contributed.