How did the vocabulary of weather (rain, wind, storm, fog, snow, sun, cloud a...

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• ✓citations 18 cite key(s) resolve in refs.bib; snapshot: 17 verified, 1 unverifiable at run time
• ✓integrity 98 files hash-verified against provenance.json; 211 table cell(s) traced in the derivation graph; 2 change(s) after export recorded as amendments (paper/paper.pdf, paper/paper.tex)
• ✓method checks text: recounted from the corpus: terms_c1=66, terms_c2=75, terms_c3=71, terms_c4=73, terms_c5=73, terms_c6=69, terms_all=86, keyness_c1=40, keyness_weather_c1=68, keyness_c2=40, keyness_weather_c2=68, keyness_c3=40, keyness_weather_c3=68, keyness_c4=40, keyness_weather_c4=68, keyness_c5=40, keyness_weather_c5=68, keyness_c6=40, keyness_weather_c6=68, n_documents=84, n_tokens=9248093, weather_all_precision=0.7333, precipitation_precision=0.875, wind_words_precision=0.875, storm_words_precision=0.4167, fog_words_precision=0.5, snow_ice_words_precision=0.8333, sun_words_precision=0.8333, cloud_words_precision=0.5833
• ✓numbers 211 table cell(s) are source values at the precision shown; text (not gating): 232 of 232 number(s) are source values, 0 differ from the quantity named nearby, 0 untraced
• ✓results contract text results (corpus statistics, term frequencies and text-model outputs) are complete and consistent
• ✓results from code written by the last run of the shipped script: estimation_results.json ← run_estimation.py
• ·spec no design/identification_spec.json
• ✓tables 4 table(s) reproduce byte-identically from the sidecars

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