Evidence map›Paper›PMID 42787391›Full record

ArticleFrontiers in research metrics and analytics2026

From existence checking to semantic auditing: citation verifiability in the age of AI.

Libor Ansorge

Abstract read
In one paragraph

Article in Frontiers in research metrics and analytics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Libor AnsorgeT. G. Masaryk Water Research Institute, Podbabská, Prague, Czechia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper reflects on the current crisis of academic integrity caused by the phenomenon of fabricated and semantically incorrect citations. In the era of 'fast science' and permanent citation inflation, human reviewers face extreme cognitive overload and time pressure, making it impossible to consistently verify literature sources. As a key solution, this paper proposes the deployment of autonomous AI agents (Agentic AI) designed for the deep semantic auditing of manuscripts. This technological shift is complemented by an innovative proposal to introduce mandatory authorial 'citation reports' that operationalize the concepts of citation classification. The resulting synergy between human expertise and machine precision relieves reviewers of their mechanical burden and can help strengthen the rigor, transparency, and credibility of peer review.

Indexed as

agentic AIartificial intelligence (AI)citation classificationcitation inflationcitation ontologycitationsfake citationssemantic auditing

Identifiers

PMID42787391
PMCPMC13601301

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.