Journal article uncovers circadian gene expression patterns in Arabidopsis thaliana, implying Gradient Mechanics relevance.
This paper presents the first biological instantiation of the Triadic Kernel {E = 4/5, C = 7/10, F = 3/5} against the Arabidopsis thaliana circadian RNA-seq dataset SRP222555 (PRJNA566413; GEO: GSE137732). The dataset was produced to study circadian clock regulation of alternative splicing and alternative polyadenylation; it was not designed to test Gradient Mechanics. From the Hardlock constants of Essays I and XIII Terminal of Gradient Cybernetics: The Calculus of Recursion, five empirical consequences are derived and tested against 20,683 genes across three Drive regimes. The derived biological gradient signal is z(g, ZT) = [log₂(TPM_LD(g,ZT)+1) − μ_CL(g)] / σ_CL(g), where μ_CL and σ_CL are the mean and standard deviation of gene g in the continuous-light (CL) zero-gradient reference samples. Gene regime is assigned from the forcing magnitude |⟨z_LD⟩| before any test statistic is computed. H1: per-gene kurtosis of z_LD across 16 Zeitgeber Time points follows strict monotonic Drive ordering (Jonckheere-Terpstra J = 75,525,173, Z = 9.1213, p < 10⁻⁸; CoLocated mean kw = −0.431 < Flat = −0.532 < Alpine = −0.535; Alpine non-Gaussian p = 0.0000). H2: super-white variance scaling confirmed on the gene forcing-magnitude axis (α = −0.486, z_stat = 15.84, p < 0.001). H3: per-observation snap fraction using r = √(14/9) = 1.2472 [H] yields Alpine 77.55% > Flat 44.14% > CoLocated 18.43%, with empirical CL null 21.05% ≈ theoretical 21.23% — the snap threshold is not a free parameter. H4: per-gene Dirac Comb log-likelihood ratio with Λ = 0.2984 [H], Δ = 0.7016 [H], r = 1.2472 [H] yields Alpine 100.0% DC-wins > Flat 99.4% > CoLocated 20.4% — full gradient order confirmed with zero free parameters. H5: the z field is continuous (n_unique/n_total = 0.9925, consistent with sub-ZT resolution). All constants are exact rational functions of {E, C, F, δ}. Zero free parameters exist at any stage. The Triadic Kernel is not falsified by any metric in the SRP222555 dataset.
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Eugene Pretorius (2026) studied this question.
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