Fix GitHub link (MujeebOnawole/PoreGCN) + robust CIF fallback parser

app.py: remove inaccurate claim that Tb-MOF reproduces same Scenario pattern under both XAI modes

app.py: rename Full to Slow + plain-language Fast/Slow XAI explainer (predicted values are the same; only attribution method differs); add optional Goatcounter visitor-tracking pixel via GOATCOUNTER_CODE env var

app.py: drop the offline-pipeline bullet from the How-to-use list

app.py: per-property XAI in Fast mode (~35s, all rows correctly classified); Full stays target-only with Fast surrogate for other rows; About tab rewritten with honest how-to-use guidance

app.py: load property_means from per-ensemble JSON; classify_scenario uses manuscript dual criteria (ratio>=0.70 OR mean signed direction)

app.py: Fast/Full XAI toggle + skip +0.00 cavity labels + About Ensembles and XAI explainer

app.py: swap bundled CoRE MOF validation example to Tb-MOF (dual-method 5 of 7 Scenario A)

app.py: wire 4th example (Zn-MOF-ChemSci2019 -> core_mof) into KNOWN_MOFS + About Ensembles

app.py: legend matches viewer (cornflower-blue metals + cavity attribution chips); slim footer with email

app.py: smaller cavity beads + per-bead attribution labels + wrap atoms to [0,1) + clip mesh to cell

app.py: manual white wireframe + xyz labels; one example per ensemble; About Ensembles tab

app.py: opaque mesh (0.55) + opaque beads + white wireframe + xyz axis labels

Wire CSV download in tab 4

Initial PoreGCN web tool deployment