```json { "scores": [ { "id": "cnv_e233f51179d547d4", "score": 78, "tier": "great", "rationale": "Concrete dimensions (482 rows, 61 columns) and precise time span (Nov 2010–Jul 2025). Essential orientation with specificity." }, { "id": "cnv_36090317cec549f0", "score": 72, "tier": "great", "rationale": "Clear definition of what dataset represents and row-level semantics. Foundational context for all downstream analysis." }, { "id": "cnv_412fbf162ef5445f", "score": 75, "tier": "great", "rationale": "Systematic column taxonomy (identifiers, dimensions, metrics, dates) with distinct value counts. Enables smart analysis planning." }, { "id": "cnv_b40a96dcec274360", "score": 68, "tier": "regular", "rationale": "Representative sample shows scale, geography, sector diversity. Useful but somewhat descriptive; lacks quantitative insight." }, { "id": "cnv_1eed90e880d04766", "score": 82, "tier": "great", "rationale": "Identifies 24 columns with missing data, ranked by severity (98% down to 1%). Critical for data quality assessment and modeling." }, { "id": "cnv_c1f24d79a1fb4d13", "score": 85, "tier": "great", "rationale": "Reveals 190 anonymized Chinese systems (40% of dataset) with exact duplicates. Major data quality issue affecting 40% of records." }, { "id": "cnv_cb3297b2315a454e", "score": 70, "tier": "regular", "rationale": "Distinct value counts for categorical columns. Useful reference but mostly confirmatory; low surprise value." }, { "id": "cnv_ddfe6a3f569548d8", "score": 76, "tier": "great", "rationale": "Distribution summaries (min/max/mean/median/SD) for all numeric columns. Essential for understanding metric ranges and spread." }, { "id": "cnv_e1de00435d534544", "score": 79, "tier": "great", "rationale": "Identifies 11 distinct data quality issues: missing data, precision artifacts, inconsistent formats, outliers. Actionable for cleaning." }, { "id": "cnv_ebf3705c5bb749aa", "score": 74, "tier": "great", "rationale": "Top values for chip types and countries with frequencies. Concrete distribution data (H100 16%, China 39%) guides segmentation." }, { "id": "cnv_a37bcd04d8ce4750", "score": 81, "tier": "great", "rationale": "Identifies extremely strong correlations (r>0.98) between power/cost/compute. Reveals underlying structure; informs feature engineering." }, { "id": "cnv_bb6bab57e2674049", "score": 77, "tier": "great", "rationale": "Identifies Max OP/s as primary metric with justification and extreme records. Clarifies what matters most in dataset." }, { "id": "cnv_c58c191a510b49d3", "score": 80, "tier": "great", "rationale": "Clear upward trend from 2010–2024 with specific inflection points (2017 acceleration, 2022 dip). Actionable temporal insight." }, { "id": "cnv_0c72e79e698743b6", "score": 62, "tier": "regular", "rationale": "Identifies November spikes and seasonal patterns. Interesting but confidence marked 'partial' and analysis appears truncated." }, { "id": "cnv_72a96fd9e2234335", "score": 83, "tier": "great", "rationale": "Identifies H100 equivalents as key metric and Owner as key category; shows variation across owners. Directly actionable for segmentation." }, { "id": "cnv_b13d04710bd54caf", "score": 79, "tier": "great", "rationale": "Proposes 4 natural segmentation schemes (scale tiers, country, owner, sector) with concrete breakdowns. Enables targeted analysis." }, { "id": "cnv_3095999ae7d448d8", "score": 81, "tier": "great", "rationale": "Geographic concentration stark: China 39%, US 25%, together 64%. Specific numbers reveal geopolitical compute divide." }, { "id": "cnv_2c100f899c0c4fae", "score": 88, "tier": "great", "rationale": "Counterintuitive finding: China has 48% more systems but only 8.7% of US compute capacity. High surprise, decision-changing insight." }, { "id": "cnv_7af1d7d4019843b0", "score": 65, "tier": "regular", "rationale": "Proposes 3 interesting research questions. Useful framing but generic; lacks concrete findings or evidence." }, { "id": "cnv_42f70bf804e34aec", "score": 73, "tier": "regular", "rationale": "Complete column reference with types and meanings. Essential reference material but primarily descriptive, not analytical." } ] } ```