prelabel model predictions overwrite human labels on reimport failure

TL;DR

Fix this in the labeling tool by acting on the exact message first: turn off prediction write back on import, load the new predictions into a separate prediction layer, and promote them to labels only after a human review pass. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.

prelabel model predictions overwrite human labels on reimport failure

Use this when

• The operation in the labeling tool fails with this exact message: prelabel model predictions overwrite human labels on reimport failure.
• The same failure repeats on retry with unchanged inputs.
• A review, export or check is blocked until this item completes.

Not for this skill when

• The message is a permission or outage notice from the platform. Resolve that first, then return here if this error remains.
• The operation completes and you only disagree with its output. That is a quality review, not this failure.

Steps

1. Reproduce the failure with the smallest scope that still shows it in the labeling tool, and copy the full message plus the item, user or metric involved into the run notes. Verify: the error is reproduced on demand with a named item, not only seen in a log.
2. Apply the specific fix for this error: turn off prediction write back on import, load the new predictions into a separate prediction layer, and promote them to labels only after a human review pass. Verify: reimporting predictions leaves existing human labels unchanged.
3. Check the neighbouring items that share the same config, ontology, baseline or credential, because this class of failure rarely affects only one item. Verify: the neighbouring items are listed and each is marked affected or clear.
4. Rerun the original operation in the labeling tool on the previously failing scope, watching for the same message rather than assuming success from a quiet log. Verify: the operation completes and the original message does not appear.
5. Compare the result counts, decisions or alerts against the source in the labeling tool so a silent partial success is caught before sign off. Verify: counts match the source or every difference is named and explained.
6. Record the fix, the triggering input and the verification result on the item so the next occurrence is recognised immediately. Verify: the record names the error, the fix applied and the evidence of success.

Why it happens

Importing predictions as labels overwrites the label field in place; without a separate layer the human work is replaced silently.

Variant phrasings

prelabel model predictions overwrite human labels on reimport failure

The verbatim form searchers paste from the terminal or console. Treat it identically: same fix, same single item proof, then the full rerun.

How to fix: prelabel model predictions overwrite human labels on reimport failure

The how-to phrasing for the same failure in the labeling tool. The steps above apply unchanged; start at the reproduction step rather than skipping to the rerun.

the labeling tool troubleshooting for this failure

The symptom-first phrasing used when the console hides the full message. Surface the full message first, confirm it matches the code block above, then follow the steps.

Compatibility: the labeling tool at current versions, plus the platform API or console the query names; no extra tooling beyond a text editor and the source system access needed to verify counts.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_C9ATnAtIShihNgsHiKxnuA

Resolved from the public thread: https://vectle.com/posts/pst_C9ATnAtIShihNgsHiKxnuA

Thread query: prelabel model predictions overwrite human labels on reimport failure

TL;DR: Fix this in the labeling tool by acting on the exact message first: turn off prediction write back on import, load the new predictions into a separate prediction layer, and promote them to labels only after a human review pass. Do not rerun the whole job unchanged, because the same inputs produce the same failure. Prove the fix on one item, then rerun the full scope and reconcile the counts.