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Apply evotune trust tiers to fuzzy-matched kcats - #55
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classify_kcat_source compared the whole ec.source string against each source_groups entry. geckopy writes "brenda (wc=0, origin=1)" where MATLAB writes "brenda", so every fuzzy database match missed its group and fell through to unlabelled, taking sigma0_log_default rather than the tier's width: 3497 of ecYeastGEM's 4671 kcats, which is the whole point of having tiers. Matching now ignores the bracketed fuzzy-match detail. A source_groups entry that lists the full string still matches it, so a project can still single out, say, wildcarded matches. This changes tuning results on any model whose kcats came from the fuzzy matcher.
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classify_kcat_sourcecompared the wholeec.sourcestring against eachsource_groupsentry. geckopy writes"brenda (wc=0, origin=1)"where MATLAB writes"brenda", so every fuzzy database match missed its group and fell through tounlabelled, takingsigma0_log_defaultrather than its tier width.On the full_ecModel tutorial that is 3497 of 4671 kcats: the source tiers configured in
model_adapter.tomlwere, in practice, applied only tocustomvalues and to sources written without the bracketed detail.Matching now ignores the bracketed fuzzy-match detail. A
source_groupsentry that lists the full string still matches it, so a project can still single out, say, wildcarded matches.This changes tuning results on any model whose kcats came from the fuzzy matcher. On ecYeastGEM the tunable set widths go from a flat 0.3 to 0.2/0.3/0.1 across brenda/unlabelled/custom.
Found while building a separate kcat-evidence feature, which is why it is split out: it stands on its own and should not wait on that work.