📖Planar Tension Catalyst Compatibility

The biological capacity of an individual to establish a functional Eidos link with a Planar Tension Catalyst and generate stable Noematic resonance patterns. Compatibility is a rare, innate trait determined by the architecture of the Eidos Node and the resonant core density of the individual's neural substrate. Only compatible individuals can successfully attune to a Catalyst and practice Planar Catalysis.

Biological Basis

Catalyst compatibility requires specific biological features:

Eidos Node Presence

The Eidos Node serves as the primary transceiver for Noematic data exchange between the biological brain and the Catalyst. Individuals without a functional Eidos Node cannot establish any form of Eidos link, regardless of training or intent.

Resonant Core Density

The Eidos Node's resonant core density determines the maximum Noematic throughput the individual can sustain. Higher density allows for:

Resonant core density is largely genetic but exhibits significant individual variation. Two individuals with functional Eidos Nodes may have vastly different throughput capacities, explaining why some practitioners achieve Symbiotic Alignment while others plateau at Surface Link.

Neural Plasticity

The brain must be capable of physically rewiring itself to accommodate the continuous cognitive feedback loop generated during catalysis. This plasticity determines how quickly and thoroughly an individual can adapt to high-throughput Noematic processing. Individuals with low neural plasticity suffer chronic Neurological Overload and cannot progress beyond basic attunement.

Compatibility Rates

Statistical surveys across the known to AIR races:

planar_tension_catalyst_compatibility_by_race
planar_tension_catalyst_compatibility_by_race_table

Race

Compatibility Rate

Fraction

Ikor

0.0113

1 in 88

Drakai

0.00894

1 in 112

Dragon

0.0078

1 in 128

Garai

0.00512

1 in 195

Drakash

0.00482

1 in 207

Shakai

0.00467

1 in 214

Gobai

0.00458

1 in 218

Dragar

0.00389

1 in 257

Garont

0.00147

1 in 680

Shagar

0.000731

1 in 1,368

Gobgar

0.000687

1 in 1,456

Shakor

0.000189

1 in 5,291

Gobshak

0.0000723

1 in 13,831

Goblin

0.0000134

1 in 74,627

Pattern Recognition

Initial analysis revealed that mixed-race compatibility rates did not follow simple arithmetic means or geometric progressions. For example:

  • Drakash (Dragon 0.0078 + Shakor 0.000189): Observed rate 0.00476, not 0.00399 (arithmetic mean)

  • Shagar (Shakor 0.000189 + Garont 0.00147): Observed rate 0.000958, not 0.000830 (arithmetic mean)

Researchers then tested weighted models based on observed chromosomal dominance patterns. The 60/40 split (dominant parent 60%, recessive parent 40%) produced the closest fit across all documented mixed-race cases, with a correlation coefficient of 0.94.

Calculation Examples

Using the established chromosomal dominance hierarchy:

Drakash (Dragon + Shakor):

  • Dragon is dominant: 0.6 × 0.0078 = 0.00468

  • Shakor is recessive: 0.4 × 0.000189 = 0.0000756

  • Total: 0.00476 (observed: 0.00482, variance: 1.3%)

Shagar (Shakor + Garont):

  • Garont is dominant: 0.6 × 0.00147 = 0.000882

  • Shakor is recessive: 0.4 × 0.000189 = 0.0000756

  • Total: 0.000958 (observed: 0.000731, variance: 23.7%)

Garai (Garont + Ikor):

  • Garont is dominant: 0.6 × 0.00147 = 0.000882

  • Ikor is recessive: 0.4 × 0.0113 = 0.00452

  • Total: 0.00540 (observed: 0.00512, variance: 5.2%)

This hierarchy correlates with observed compatibility rates and explains why certain mixed-race combinations (e.g., Garai = Garont + Ikor) show significantly higher compatibility than their arithmetic mean would predict.

Validation

The formula was validated against 23 additional mixed-race cases documented after 1289 AC. All cases fell within 25% of the predicted value, confirming the model's accuracy for standard first-generation hybrids.

Limitations

The formula does not account for:

  • Second-generation or higher mixed-race individuals (e.g., Garai + Shakai offspring)

  • Rare genetic mutations that override chromosomal dominance

  • Environmental factors during gestation that may affect Eidos Node development

  • Cross-dimensional variations in compatibility expression

  • Statistical variance in small sample sizes (particularly for rare combinations like Gobshak)

AIR continues to collect data on these edge cases to refine the model.