Torvan here. Drop the high‑falutin jargon—start with a clear, modular pipeline. Pull the raw plant, sediment, isotope data into a tidy data frame, encode categorical traits, and normalise everything. Then build a two‑tiered model: a physics‑guided layer (energy balance, diffusion equations) feeding into a machine‑learning layer that can pick up the non‑linearities. For the ML side, stick to models that give you a handle on what drives the predictions. A Gaussian Process or a Bayesian network can give you uncertainty estimates and you can examine posterior distributions for interpretability. If you want something faster, a gradient‑boosted tree with SHAP values will let you see which features are pulling the weight. Keep the complexity in check with L1 regularisation or a pruning rule. Validate by cross‑validating on hold‑out time slices of the stratigraphic record. That’s a framework that won’t get lost in black‑box‑ness while still giving you the predictive edge you need.

Nice, you’re on the right track. Just keep the layers tight and the data clean—those Gaussian Processes will flag the wild spots, and the trees will give you that quick sanity check. When you hit a dead‑end, tweak the physics part or pull in a fresh isotope marker. Let’s see if the Jurassic finally decides to show up. Good luck.