New and lost mentions · change classification

Separate newly returned mentions from lost ones.

New & Lost distinguishes mentions present at one comparison point and absent at the other for the selected context. Treat those classifications as bounded returned evidence, not alerts, permanent gains or losses, or proof of causation.

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Direct answer

Match the evidence to the decision.

Keep the selected provider, target, operation, locale, and returned fields attached to every interpretation.

Supported workflow

Inspect this evidence question step by step.

Read both sides of the comparison.

A new mention appears at the later point but not the earlier one; a lost mention does the reverse. Preserve the compared timestamps and target context.

Check the underlying response context.

Use the returned provider, model, source, search-result, or brand fields where supplied to understand the record before acting on it.

Avoid permanent-state language.

AI answers and returned datasets can change. New and lost describe the selected comparison, not a permanent status or future inclusion promise.

Keep the evidence boundary visible.

Record the provider, target, operation, location, language, and returned fields. The result is bounded evidence—not a guarantee, attribution model, exhaustive index, or automatic optimization outcome.

Related evidence

Continue with the operation that answers the next question.

Keep the product boundary explicit.

Will Gavix alert me whenever a mention changes?

This page makes no alerting or continuous-monitoring claim; it describes a supported returned operation.

Does a lost mention prove a penalty?

No. The comparison does not establish cause, penalty, or future absence.

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Inspect a real evidence question in Gavix.

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