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This study uses plasma-profile data from the EUROfusion pedestal database,focusing on the electron-temperature and electron-density profiles in the edgeregion of H-mode ELMy JET ITER-Like-Wall (ILW) pulses. We make systematicpredictions of the electron-temperature pedestal, using the density profilesand engineering parameters of the pulses as inputs.  We first present a machine-learning algorithm that, given more inputs thantheory-based modelling and 80\% of the database as training data, canreconstruct the remaining 20\% of temperature profiles within 20\% of theexperimental values, including accurate estimates of the pedestal width andlocation. The most important engineering parameters for these predictions aremagnetic field strength, particle fuelling rate, plasma current, andstrike-point configuration. This confirms the potential of accurate pedestalprediction using large databases.  Next, we take a simple theoretical approach assuming a local power-lawrelationship between the gradients of density ($R/L_{n_e}$) and temperature($R/L_{T_e}$): $R/L_{T_e}=A\left(R/L_{n_e}\right)^\alpha$ with $\alpha\approx0.4$ fits well in the steep-gradient region. When $A$ and $\alpha$ are fitindependently for each pedestal, a one-to-one correlation emerges, also validfor JET-C data. For $\alpha = 1$, $A \equiv \eta_e$, a known control parameterfor turbulence in slab-ETG theory. Measured values of $\eta_e$ in thesteep-gradient region lie well above the slab-ETG stability threshold,suggesting a nonlinear threshold shift or a supercritical turbulent state.  Finally, we test heat-flux scalings motivated by gyrokinetic simulations, andwe provide best-fit parameters for reconstructing JET-ILW pedestals. Thesemodels require additional experimental inputs to reach the accuracy of themachine-learning reconstructions.

Reconstructions of electron-temperature profiles from EUROfusion Pedestal Database using turbulence models and machine learning