Evidence map›Paper›PMID 40421069›Full record

ArticleEcology and evolution2025

Predicting the Activity Level of the Great Gerbil (

Fan Jiang, Peng Peng, Zhenting Xu, Yu Xu, Ding Yang, Shouquan Chai, Shuai Yuan, Limin Hua, Dawei Wang, Xuanye Wen

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Ecology and evolution, 2025. 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

10 authors.

Fan JiangCenter for Biological Disaster Prevention and Control National Forestry and Grassland Administration Shenyang China.
Peng PengCenter for Biological Disaster Prevention and Control National Forestry and Grassland Administration Shenyang China.
Zhenting XuCenter for Biological Disaster Prevention and Control National Forestry and Grassland Administration Shenyang China.
Yu XuCenter for Biological Disaster Prevention and Control National Forestry and Grassland Administration Shenyang China.
Ding YangCenter for Biological Disaster Prevention and Control National Forestry and Grassland Administration Shenyang China.
Shouquan ChaiCenter for Biological Disaster Prevention and Control National Forestry and Grassland Administration Shenyang China.
Shuai YuanCollege of Grassland, Resources and the Environment Inner Mongolia Agricultural University Hohhot China.
Limin HuaKey Laboratory of Grassland Ecosystem of the Ministry of Education College of Grassland Science, Gansu Agricultural University Lanzhou China.ORCID https://orcid.org/0000-0002-4651-6702
Dawei WangWestern Agricultural Research Center Chinese Academy of Agricultural Sciences Changji China.ORCID https://orcid.org/0009-0007-9183-6207
Xuanye WenCenter for Biological Disaster Prevention and Control National Forestry and Grassland Administration Shenyang China.ORCID https://orcid.org/0000-0002-0232-4987

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The great gerbil (

Indexed as

extreme learning machinemachine learningneural networkspopulation monitoringRhombomys opimus

Identifiers

PMID40421069
PMCPMC12104662

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.