```json { "scores": [ { "id": "cnv_0cbff8d67083453e", "score": 72, "tier": "regular", "rationale": "Clear orientation: 3,574 AI models, 61 attributes. Useful but foundational—expected first question." }, { "id": "cnv_d750df3bc2d54004", "score": 68, "tier": "regular", "rationale": "Solid schema breakdown (identifiers, categorical, numeric, dates). Necessary reference but low surprise." }, { "id": "cnv_4353ae71cc0b4d59", "score": 65, "tier": "regular", "rationale": "Representative sample with concrete examples (DeepSeek, K-EXAONE). Helpful orientation but surface-level." }, { "id": "cnv_b7cab67073b04208", "score": 78, "tier": "great", "rationale": "Critical finding: 20 columns missing data; post-training compute 99.97% missing. Directly impacts analysis feasibility." }, { "id": "cnv_2c816c3917bc4678", "score": 62, "tier": "regular", "rationale": "1 exact duplicate, 4 near-duplicates identified. Useful data-quality check but minimal impact (0.03%)." }, { "id": "cnv_b41fe7e0162b41d3", "score": 70, "tier": "regular", "rationale": "Cardinality counts for categorical columns (Model: 3,569; Organization: 1,315). Useful reference, low surprise." }, { "id": "cnv_faa41740e27f457f", "score": 75, "tier": "regular", "rationale": "Complete column glossary with 61 definitions. Essential reference material but purely descriptive." }, { "id": "cnv_defdf39de0344446", "score": 80, "tier": "great", "rationale": "Distribution summaries reveal right-skewed citations (median 493 vs mean 4,570), parameters span 10–173.9T. Actionable for modeling." }, { "id": "cnv_148a6869407346cb", "score": 85, "tier": "great", "rationale": "Identifies 7 major data-quality issues: 60.8% training compute missing, duplicates, inconsistent units. Critical for analysis planning." }, { "id": "cnv_fb7b95ef2d084c68", "score": 73, "tier": "regular", "rationale": "3,574 rows, 61 columns, 76-year span (1950–2026). Foundational but expected metadata." }, { "id": "cnv_5094aa16ea62424f", "score": 76, "tier": "regular", "rationale": "Domain breakdown: Language 44%, Biology 10.5%, Vision 9%. Useful segmentation but straightforward frequency analysis." }, { "id": "cnv_e32c574a56664b2b", "score": 82, "tier": "great", "rationale": "Training cost ↔ compute r=0.986, chip-hours ↔ compute r=0.981. Strong correlations validate data integrity and enable proxy metrics." }, { "id": "cnv_0c8a513f906a4e98", "score": 79, "tier": "great", "rationale": "Training compute identified as central metric; 26-order-of-magnitude range (40–5×10²⁶ FLOP). Concrete, actionable focus." }, { "id": "cnv_08f0b63f1f844428", "score": 74, "tier": "regular", "rationale": "Clear exponential growth trend 1950–2026, with acceleration post-2010. Expected pattern, partial evidence." }, { "id": "cnv_63b47e1af0d545d9", "score": 71, "tier": "regular", "rationale": "2009 spike (22 models), 2006 spike (17 models). Identifies temporal anomalies but limited context on causes." }, { "id": "cnv_8b92753fb1414917", "score": 88, "tier": "great", "rationale": "Training compute varies 4,000× across Approach types; Transformers dominate. Directly actionable for segmentation and strategy analysis." }, { "id": "cnv_4c8bc6c6890a45c8", "score": 81, "tier": "great", "rationale": "Multiple segmentation paths (Domain, Approach, Organization, Country). Enables flexible analytical frameworks; high utility." }, { "id": "cnv_132cbd03240b4d70", "score": 84, "tier": "great", "rationale": "US dominates 33.6%, China 16.3%, together 50%. Geographic concentration is strategic insight for policy/investment analysis." }, { "id": "cnv_27938e4619b14fa2", "score": 92, "tier": "great", "rationale": "Counterintuitive: tiny models (1.5M params) require 50,000× more FLOP/param than large ones. Surprising, specific, challenges assumptions." }, { "id": "cnv_cd8a885dd93d45b4", "score": 86, "tier": "great", "rationale": "Three high-impact research questions identified (scaling race, geographic competition, capability-compute tradeoffs). Guides next analysis." } ] } ```