Fix the label correction for the train split

An AI wrote a label fix before training. It took the train rows with train = df[df['split'] == 'train'], then ran train['label'][train['raw_score'] > 0.9] = 1. Pandas raised a SettingWithCopyWarning, the assistant said to ignore it, and the corrected labels never reached the model. Your team rewrote the step in plain code.

An assistant wrote relabel_train_split(splits, labels, raw_scores, threshold). It is in the editor. It has a bug. Find it and fix it.

Input and output:

• splits, labels and raw_scores: one entry per row, at the same index: the row's split, its label, and its audit score.
• threshold: train rows scoring above this get label 1.
• Return the corrected labels of the train rows, in row order.

Rules the tests check:

• If the lists differ in length, use only the first n rows, where n is the shortest length.
• Only rows whose split is exactly train are returned, case included.
• A train row whose score is strictly greater than threshold gets label 1. A score equal to it keeps its label.
• Every other train row keeps its label.