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tegwick a9ca0adfcf feat(example): add per-entity LLM evaluations for 985 WoN entities (S3.3)

Batch evaluation of all 988 entities via OpenRouter. 984 succeeded on
first pass; 3 failed (network errors). eval-summary --update-metrics
written with per_entity_mean=3.9556.

Viability dashboard: 6/6 PASS
  redundancy_ratio   0.0061  (max 0.10)
  coverage_ratio     0.6190  (min 0.40)
  coherence_comps    0.0000  (max 3)
  consistency_cycles 0.0000  (max 0)
  granularity_entropy 2.6748 (min 1.0)
  per_entity_mean    3.9556  (min 3.5)

Dimension breakdown (mean across 985 entities):
  definition_precision  3.62
  source_grounding      4.36
  domain_placement      4.56
  vsm_relevance         3.31
  explanatory_value     3.94

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

2026-02-23 09:36:46 +01:00

3.4 KiB

Raw Blame History

entity_slug, evaluator, evaluated_at, overall_score, scores

entity_slug

evaluator

evaluated_at

overall_score

scores

agricultural_price_volatility

null

2026-02-23T00:30:45.056374

4.0

name	value	max_value	rationale
definition_precision	4.0	5.0	The definition clearly captures the concept of price fluctuations in agricultural markets and identifies specific causal factors (supply, demand, weather, production changes). It avoids circularity and distinguishes this from general price movements by focusing on the volatility aspect.

name	value	max_value	rationale
source_grounding	4.0	5.0	Smith does extensively discuss agricultural price fluctuations in Book I, Chapter 11, examining how corn prices vary with harvests and market conditions. The entity accurately reflects his analysis of both short-term disruptions and long-term price trends in agricultural markets.

name	value	max_value	rationale
domain_placement	5.0	5.0	"Exchange" is the correct domain placement since agricultural price volatility is fundamentally about how prices fluctuate in market transactions. This belongs squarely within exchange mechanisms rather than production, distribution, or consumption domains.

name	value	max_value	rationale
vsm_relevance	3.0	5.0	This entity has moderate VSM relevance, primarily mapping to S2 (coordination/anti-oscillation) as price volatility represents oscillatory behavior that markets must manage. It also touches on S4 (environmental adaptation) as markets respond to external shocks like weather.

name	value	max_value	rationale
explanatory_value	4.0	5.0	The entity provides good explanatory value by illuminating how agricultural markets function differently from other markets due to production uncertainties and seasonal factors. It helps explain market dynamics and the challenges of economic planning in agriculture.

Evaluation: Agricultural Price Volatility

definition_precision — 4.0 / 5.0

The definition clearly captures the concept of price fluctuations in agricultural markets and identifies specific causal factors (supply, demand, weather, production changes). It avoids circularity and distinguishes this from general price movements by focusing on the volatility aspect.

source_grounding — 4.0 / 5.0

Smith does extensively discuss agricultural price fluctuations in Book I, Chapter 11, examining how corn prices vary with harvests and market conditions. The entity accurately reflects his analysis of both short-term disruptions and long-term price trends in agricultural markets.

domain_placement — 5.0 / 5.0

"Exchange" is the correct domain placement since agricultural price volatility is fundamentally about how prices fluctuate in market transactions. This belongs squarely within exchange mechanisms rather than production, distribution, or consumption domains.

vsm_relevance — 3.0 / 5.0

This entity has moderate VSM relevance, primarily mapping to S2 (coordination/anti-oscillation) as price volatility represents oscillatory behavior that markets must manage. It also touches on S4 (environmental adaptation) as markets respond to external shocks like weather.

explanatory_value — 4.0 / 5.0

The entity provides good explanatory value by illuminating how agricultural markets function differently from other markets due to production uncertainties and seasonal factors. It helps explain market dynamics and the challenges of economic planning in agriculture.

3.4 KiB Raw Blame History