code wiki / _hdl_build / nx_game_genetics_gate.nx

nx_game_genetics_gate.nx

buildroot/runtime/_hdl_build/nx_game_genetics_gate.nx

17387 B338 linesdepth 3pulls 3 transitivereach 0 importersview sourcekind gate/prooftopic game

docs dependencies structs consts functions

about

nx_game_genetics_gate.nx -- proves the genetics core teaches REAL science by MEASURING the textbook ratios out of the running simulation. Nothing here is asserted: every tooth crosses thousands of virtual creatures and checks the emergent numbers against what Mendel/Morgan/waddington predict. If a tooth is green, a student could reproduce that number with a pencil and a Punnett square. MUTATION TARGET (documented): break segregation in gx_gamete (always pass allele 0) => T1 monohybrid ratio collapses to 4:0 => RED, while the independent-assortment and epigenetic teeth are unaffected. license_tier: ORIGINAL

dependencies 2 imports · 0 importers

imports: nx_syscalls.nxnx_game_genetics.nx

imported by: nobody (leaf or entry point)

call flow from main pre-order; caps 40 nodes / depth 6 declared; ↻ = already shown

structs

none

consts

43	const N: i64 = 8000 // sample size: big enough that a 3:1 lands inside +/-40 permil
44	const TOL: i64 = 45 // permil tolerance on every predicted ratio

functions

12	func gg_cat(b: u8, off: i64, s: u8) -> i64 { var o: i64 = off; var i: i64 = 0; while s[i] != (0 as u8) { b[o] = s[i]; o = o + 1; i = i + 1 } return o } called by 1: main
13	func gg_catn(b: *u8, off: i64, v: i64) -> i64 called by 1: main calls 1: sys_mmap
26	func p(s: *u8) -> i64 { var n: i64 = 0; while s[n] != (0 as u8) { n = n + 1 } sys_write(1, s, n); return 0 } called by 2: nl main calls 1: sys_write
27	func pn(v: i64) -> i64 called by 1: main calls 2: sys_mmap sys_write
41	func nl() -> i64 { p("\n" as *u8); return 0 } called by 1: main calls 1: p
46	func near(got: i64, want: i64) -> i64 called by 1: main
53	func main() -> i64 calls 30: p sys_mmap gx_set gx_cross gx_a0 gx_a1+24