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Geographically, sightings concentrate heavily in Washington (631), California (431), and Ohio (317), and the most common county is Pierce \u2014 worth a closer look as the data skews toward the Pacific Northwest. Temporally, the year distribution is left-skewed (mean 1997, median 2001, range 1870\u20132025), so most reports come from the late 1990s onward, and August/October/July dominate the month field, hinting at a warm-season reporting pattern. Classification is nearly a coin-flip between Class A (2,655) and Class B (2,722), with Class C almost absent (34) \u2014 that imbalance is something to flag before any modeling. Note also that 338 county values are empty even though state coverage is complete.","scope":"dataset","target":"__global__"},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.min","stats.max","stats.mean","stats.median","stats.skew"],"model":"anthropic:claude-opus-4-7","narrative":"This column is almost certainly a row identifier: all 5411 values are unique with no nulls, spanning 60 to 79711. The wide range relative to the row count suggests sparse, non-sequential IDs (likely assigned from a larger source system) rather than a dense 1..N index. Skew of 0.91 and median 16598 vs mean 23288 are expected artifacts of ID allocation, not meaningful distribution signals.","role":"identifier","scope":"column","target":"id","treatment":"Exclude from modelling; retain as a join key."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.cardinality","stats.entropy_ratio","stats.top_rate","stats.top_value","top_values"],"model":"anthropic:claude-opus-4-7","narrative":"This is a US state field with 53 distinct values across 5411 rows and no nulls, suggesting it includes the 50 states plus a few extras like DC or territories. Distribution is fairly even (entropy ratio 0.877), but Washington leads at 11.66% (631 rows) \u2014 unusually high for a national sample and ahead of California (431), hinting at geographic bias toward the Pacific Northwest.","role":"feature","scope":"column","target":"state","treatment":"One-hot or target-encode; consider grouping the long tail and noting the Washington over-representation."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.cardinality","stats.entropy_ratio","stats.top_rate","stats.top_value","top_values"],"model":"anthropic:claude-opus-4-7","narrative":"This column holds US state codes as two-letter lowercase abbreviations, with 53 distinct values across 5411 rows and no nulls \u2014 slightly more than the 50 states, suggesting territories or DC are included. Distribution is broad (entropy ratio 0.88) but tilts toward Washington (wa, 11.7%) and California (ca, 431), which is unusual since wa outranks ca despite California's larger population.","role":"feature","scope":"column","target":"state_code","treatment":"Use as a categorical feature; one-hot or target-encode for modelling."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.duplicate_rate","stats.n_empty","stats.one_word_rate","stats.word_mean","stats.len_max","top_values","top_words"],"model":"anthropic:claude-opus-4-7","narrative":"Single-word US county names (e.g., Pierce, Jefferson, Lewis, Snohomish, Skamania) acting as a geographic categorical feature. Heavy duplication is expected for this kind of field (duplicate_rate 0.81, 1022 unique values across 5411 rows), but 338 empty strings are recorded as non-null and should be treated as missing. The county mix (Snohomish, Skamania, Pierce, King) skews toward Washington/Pacific Northwest, with some overlap names like Jefferson and Jackson appearing in many states.","role":"feature","scope":"column","target":"county","treatment":"Convert empty strings to nulls and encode as a categorical (target/frequency encode given ~1k levels)."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.url_rate","stats.one_word_rate","stats.duplicate_rate","stats.len_min","stats.len_max","top_words"],"model":"anthropic:claude-opus-4-7","narrative":"This column holds a unique BFRO report URL for each of the 5411 rows, all following the pattern https://www.bfro.net/gdb/show_report.asp?id=<n>. Every value is distinct (n_unique=5411, duplicate_rate=0.0), non-null, and url_rate is 1.0, so it functions as a per-row record locator rather than a feature. Lengths are tightly bound between 46 and 49 characters, consistent with only the numeric id varying.","role":"identifier","scope":"column","target":"url","treatment":"Keep as a row-level link for traceability; drop from modelling or extract the trailing id as a foreign key."},{"confidence":"high","critiques":[],"evidence_keys":["n_unique","null_rate","stats.cardinality","stats.top_value","stats.top_rate","stats.entropy_ratio","top_values"],"model":"anthropic:claude-opus-4-7","narrative":"This column captures the month name of an event, with August (634), October (632), and July (618) leading \u2014 consistent with a summer/autumn-skewed seasonal pattern. Cardinality is 32, far above the expected 12, so there are 20 extra non-month tokens polluting the field that need investigation. Null rate is 2.96% and entropy ratio is 0.76, indicating reasonably spread but not uniform distribution.","role":"feature","scope":"column","target":"month","treatment":"Normalize to the 12 canonical months, then one-hot or cyclically encode."},{"confidence":"high","critiques":[],"evidence_keys":["min","max","n_unique","median","q1","q3","skew","n_outliers","outlier_rate","null_rate"],"model":"anthropic:claude-opus-4-7","narrative":"Year of record, ranging from 1870 to 2025 across 99 distinct values with a median of 2001 and IQR spanning 1987-2009. The distribution is left-skewed (skew -0.97) with 49 outliers (0.9%) on the early-year tail, and 1.05% of rows are null.","role":"timestamp","scope":"column","target":"year","treatment":"Treat as a temporal feature; consider bucketing by decade or clipping pre-1970 outliers before modelling."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.top_value","stats.top_rate","stats.entropy","top_values"],"model":"anthropic:claude-opus-4-7","narrative":"A three-level categorical label (Class A, B, C) with no nulls across 5411 rows. The distribution is essentially binary in practice: Class B (50.3%) and Class A are nearly tied, while Class C appears only 34 times, making it a rare class that will be hard to model or evaluate.","role":"label","scope":"column","target":"classification","treatment":"One-hot or ordinal encode; consider stratified splits or merging Class C given its rarity."},{"confidence":"high","critiques":[],"evidence_keys":["n","n_unique","null_rate","stats.len_mean","stats.len_median","stats.len_p95","stats.word_mean","stats.vocab_size","stats.duplicate_rate","stats.boilerplate_rate","stats.readability_flesch_mean","top_words","alerts"],"model":"anthropic:claude-opus-4-7","narrative":"Short free-text descriptions, almost certainly sighting summaries: 5407 of 5411 values are unique with a mean length of 67 characters and ~10.6 words. The vocabulary of 7169 tokens is dominated by 'near', 'sighting', and 'possible', suggesting templated phrasings about location-based observations. Duplicates and boilerplate are negligible (4 dupes, boilerplate_rate 0.00018), and Flesch ~55.7 indicates fairly readable prose with no URLs or emoji.","role":"free_text","scope":"column","target":"description","treatment":"Tokenize and embed (or extract entities like species/location) before modelling; do not use as a key."}],"providers":["anthropic:claude-opus-4-7"],"total_usage":{"completion_tokens":3008,"prompt_tokens":11812,"total_tokens":14820}},"language_counts":{},"meta":{"generated_at":"2026-05-01T18:06:57+00:00","mode":"full","row_count":5411,"sampled_rows":5411,"seed":42,"source":"/home/coolhand/html/datavis/data_trove/data/wild/bigfoot_sightings.json"},"notes":[],"saturn_version":"0.2.0","schema":{"classification":"categorical","county":"text","description":"text","id":"numeric","month":"categorical","state":"categorical","state_code":"categorical","url":"text","year":"numeric"}}