The calibrated thermal channel in infrared person detection comprises two structurally orthogonal axes. True calibrated $ΔT$ outperforms scale-matched grayscale pseudo-$ΔT$ by $+0.0221$ mAP (paired Wilcoxon $p=0.0098$, Cohen's $d=+1.08$), confirming that the calibrated channel carries information the grayscale channel lacks. The calibration axis is driven by the joint pairing-scale condition. Preserving both paired correspondence and absolute calibrated scale raises expected calibration error (ECE) from $\approx 0.05$ to $0.142$. The $2\times 2$ interaction ($p=0.0059$) localizes the rise to this condition. Breaking either factor alone returns ECE to the broken-factor level (true-vs-const $p=0.002$; true-vs-shuffled $p=0.002$). Under post-hoc temperature scaling, the pairing cost survives recalibration ($p=0.002$), while the scale cost is largely absorbed ($p=0.16$). As regards the detection axis, the gain stems from structural de-structuring. Phase randomization, which destroys spatial structure while preserving per-sample variance, yields $+0.0185$ mAP over true $ΔT$ on FLIR ADAS ($p=0.014$, $d=+0.96$). The FLAME~3 gain is $+0.0064$ mAP ($p=0.078$, n=8). On the calibration axis, the same intervention is insignificant on FLIR ADAS ($p=0.47$) but significant on FLAME~3 ($p=0.0078$). These findings rest on a five-arm controlled attribution with temperature-evidential learning (TEL) as the testbed. TEL obtains a single evidence-derived routing scalar together with a complementary diagnostic entropy, and establishes a finite-evidence degeneracy theorem proving that the acquisition product degenerates to this scalar with a closed-form bound. A physical $0.5^\circ$C threshold admits cross-dataset physical readings, whereas a gray threshold of $20$ inverts without physical interpretation.

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