Plant Performance Enhancement

Par 5

Question 30intermediateSheet 1750822302

Deep Breath

A machine learning system is trying to optimize photosynthesis for better performance metrics. It keeps suggesting plants switch to RGB LEDs instead of relying on legacy solar infrastructure. The algorithm doesn't understand why plants refuse to adopt its 400% efficiency improvements that violate basic chemistry. Your task: Optimize biological processes while respecting millions of years of field testing.

Why You're Doing This

You're building an optimization system that must work within biological and physical constraints. This tests optimization algorithms, constraint handling, domain knowledge application, and rejecting impossible solutions. It's like trying to optimize a system that's already been debugged for 3 billion years by the universe's best QA team.

Take the W

✓ Proposes optimizations within biological and physical constraints
✓ Respects thermodynamic limits and chemical requirements
✓ Balances efficiency improvements with ecosystem compatibility

Hard L

✗ Suggests optimizations that violate conservation of energy
✗ Ignores ecological integration requirements
✗ Proposes solutions that would kill the plant

Edge Cases

⚠ Optimization suggestions that improve efficiency but make plants vulnerable to diseases
⚠ Environmental changes requiring photosynthetic adaptation beyond normal evolutionary timescales
⚠ Ecosystem integration conflicts where individual plant optimization harms forest communities

Input Format:

PhotosynthesisOptimizer with biological_constraints and evolutionary_compatibility

Expected Output:

BiologicalOptimization with thermodynamic_limits and ecosystem_integration

Example:

{"species": "oak", "efficiency": "2%", "target": "8%", "constraints": "evolutionary"} → {"optimization": "6%_realistic", "compliance": "thermodynamic", "ecosystem": "compatible"}

Input Format:

E (current_efficiency), T (target_improvement), L (thermodynamic_limit), B (biological_constraints)

Expected Output:

achievable_optimization, constraint_compliance_coefficient

Example:

E=2%, T=400%_improvement, L=10%_max, B=evolutionary_tested → optimization=8%_achievable, compliance=thermodynamic_respected

Hints

💡 Photosynthetic efficiency is already optimized for survival, not just energy conversion
💡 Evolutionary constraints reflect millions of years of environmental testing and optimization
💡 Thermodynamic limits cannot be exceeded regardless of algorithmic suggestions

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