Turbulance DSL Quick Reference

Essential Syntax

Basic Research Protocol Structure

proposition YourHypothesisName:
    motion Hypothesis("Your scientific hypothesis statement")
    
    sources:
        local("data/your_dataset.csv")
        domain_expert("your_field")
    
    within experiment:
        given condition > threshold:
            item analysis = pipeline_stage("stage_name", config)
            ensure validation_criteria < threshold

funxn execute_research():
    return complete_analysis()

Four-Sided Triangle Pipeline Integration

Function	Pipeline Stage	Purpose
pipeline_stage("query_processor", config)	Stage 0	Query structuring
pipeline_stage("semantic_atdb", config)	Stage 1	Semantic optimization
pipeline_stage("domain_knowledge", config)	Stage 2	Expert consultation
pipeline_stage("reasoning_optimization", config)	Stage 3	Mathematical reasoning
pipeline_stage("solution_generation", config)	Stage 4	Solution generation
pipeline_stage("response_scoring", config)	Stage 5	Quality assessment
pipeline_stage("response_comparison", config)	Stage 6	Ensemble diversification
pipeline_stage("threshold_verification", config)	Stage 7	Final verification

Common Configuration Patterns

Domain Knowledge Stage

item expert_analysis = pipeline_stage("domain_knowledge", {
    expert_models: ["sprint_expert", "biomechanics_expert"],
    focus: "specific_research_area",
    analysis_depth: "advanced",
    consensus_threshold: 0.8
})

Reasoning Optimization Stage

item optimization = pipeline_stage("reasoning_optimization", {
    objective: "minimize_cost_maximize_efficiency",
    optimization_method: "multi_objective",
    constraints: previous_analysis.limitations,
    solver_type: "mathematical" // or "llm_based"
})

Solution Generation Stage

item solutions = pipeline_stage("solution_generation", {
    strategy: optimization_results,
    diversity_requirement: 0.7,
    confidence_interval: 0.95,
    solution_count: 5
})

Quality Assessment Stage

item quality = pipeline_stage("response_scoring", {
    metrics: ["accuracy", "biological_plausibility", "practical_applicability"],
    validation_method: "cross_validation",
    minimum_confidence: 0.8
})

Scientific Constructs

Hypotheses

proposition ResearchName:
    motion Hypothesis("Clear, testable scientific hypothesis")
    motion AlternativeHypothesis("Alternative explanation if needed")
    
    sources:
        local("path/to/data.csv")
        domain_expert("field_name")
        external_api("research_database")

Experimental Conditions

within experiment:
    given sample_size >= 30:
        // Your analysis steps
        
    given confidence_level > 0.95:
        // High-confidence analysis path
        
    alternatively:
        // Fallback analysis method

Validation Requirements

ensure statistical_significance(results) < 0.05
ensure effect_size(analysis) > 0.3
ensure biological_plausibility(findings) > 0.8
ensure reproducibility(protocol) > 0.9

Data Sources

Local Data

sources:
    local("relative/path/to/data.csv")
    local("/absolute/path/to/dataset.json")

Domain Experts

sources:
    domain_expert("sprint_biomechanics")
    domain_expert("endocrinology") 
    domain_expert("machine_learning")

External APIs

sources:
    external_api("pubmed_search")
    external_api("clinical_trials_database")
    external_api("genomics_repository")

Auto-Generated Files

When you submit a .trb file, the system automatically creates:

.fs - Network Visualization

Shows real-time execution progress and system consciousness state.

.ghd - Resource Dependencies

Lists all required models, data sources, and computational resources.

.hre - Decision Memory

Logs all system decisions with reasoning and confidence scores.

Common Research Patterns

Sprint Performance Analysis

proposition SprintOptimization:
    motion Hypothesis("Training protocol X improves 400m performance")
    
    sources:
        local("athlete_data/performance_metrics.csv")
        domain_expert("sprint_biomechanics")
    
    within performance_study:
        given athlete_count >= 20:
            item baseline = analyze_current_performance(data)
            item biomechanics = pipeline_stage("domain_knowledge", {
                expert_models: ["sprint_expert", "biomechanics_expert"],
                focus: "400m_race_optimization"
            })
            item protocol = pipeline_stage("reasoning_optimization", {
                objective: "maximize_400m_time_improvement",
                constraints: biomechanics.safety_limits
            })
            ensure improvement_significance(protocol.results) < 0.05

Medical Research

proposition ClinicalTrial:
    motion Hypothesis("Treatment Y reduces symptoms by >30%")
    
    sources:
        local("clinical_data/patient_outcomes.csv")
        domain_expert("clinical_medicine")
        external_api("clinical_trials_database")
    
    within clinical_study:
        given patient_cohort >= 100:
            item baseline_symptoms = assess_baseline(patients)
            item treatment_analysis = pipeline_stage("domain_knowledge", {
                expert_models: ["clinical_expert", "pharmacology_expert"],
                focus: "treatment_efficacy"
            })
            item statistical_analysis = pipeline_stage("reasoning_optimization", {
                analysis_type: "randomized_controlled_trial",
                primary_endpoint: "symptom_reduction"
            })
            ensure clinical_significance(treatment_analysis.effect_size) > 0.3

Data Science Research

proposition MachineLearningStudy:
    motion Hypothesis("Algorithm Z outperforms current state-of-the-art")
    
    sources:
        local("datasets/benchmark_data.json")
        domain_expert("machine_learning")
    
    within ml_experiment:
        given dataset_size >= 10000:
            item preprocessing = clean_and_prepare(raw_data)
            item model_development = pipeline_stage("reasoning_optimization", {
                optimization_type: "hyperparameter_tuning",
                validation_strategy: "k_fold_cross_validation"
            })
            item performance_comparison = pipeline_stage("response_comparison", {
                baseline_models: ["current_sota", "standard_benchmarks"],
                metrics: ["accuracy", "f1_score", "computational_efficiency"]
            })
            ensure statistical_superiority(performance_comparison) < 0.01

Integration with Four-Sided Triangle

Accessing Specialized Models

// Use your existing sprint expert model
item sprint_analysis = pipeline_stage("domain_knowledge", {
    expert_models: ["sprint_expert"],
    query_focus: "400m_pacing_strategy"
})

// Access biomechanics expert
item biomech_analysis = pipeline_stage("domain_knowledge", {
    expert_models: ["biomechanics_expert", "sprint_expert"],
    consensus_required: true
})

Leveraging GQIC Resource Allocation

// High-priority analysis gets more resources
within high_priority_research:
    item complex_analysis = pipeline_stage("reasoning_optimization", {
        resource_priority: "high",
        computational_budget: "unlimited",
        quality_threshold: 0.95
    })

Working with Process Monitor

// Set quality thresholds for automatic refinement
item analysis = pipeline_stage("solution_generation", {
    quality_monitor: {
        minimum_confidence: 0.8,
        maximum_refinement_iterations: 3,
        auto_refinement: true
    }
})

Error Handling

Graceful Degradation

within experiment:
    given optimal_conditions_met:
        item full_analysis = complete_pipeline()
    alternatively:
        item simplified_analysis = reduced_pipeline()

Validation Failures

// Handle validation failures gracefully
item results = pipeline_stage("threshold_verification", {
    failure_handling: "graceful_degradation",
    fallback_strategy: "reduced_confidence_analysis"
})

Performance Tips

1. Parallel Execution: Independent analyses run automatically in parallel
2. Resource Optimization: GQIC allocates resources based on full protocol context
3. Cached Results: Identical pipeline stages with same config are cached
4. Incremental Processing: Large datasets are processed incrementally

File Organization

research_project/
├── hypothesis.trb              # Your research protocol
├── hypothesis.fs               # Auto-generated visualization  
├── hypothesis.ghd              # Auto-generated dependencies
├── hypothesis.hre              # Auto-generated decision log
└── data/
    ├── raw_data.csv
    └── processed_results.json

This quick reference covers the essential patterns for integrating Turbulance DSL with Four-Sided Triangle’s specialized pipeline system.