Evidence map›Paper›PMID 41575987›Full record

ArticlePloS one2026

Recovery of expected salary estimated by facial emotion scores against computer-based landscape data.

Ni Zheng, Haiman Fu

Abstract read
In one paragraph

Article in PloS one, 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

2 authors.

Ni ZhengDaxing Airport Comprehensive Coordination Office, Beijing, China.ORCID https://orcid.org/0009-0001-6871-8874
Haiman FuIndustrial Development Planning Institute of National Forestry and Grassland Administration, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The perceived recovery of expected salary (RES) matters for work efficacy at a given amount of wage investment. A total of 31 industrial parks (IPs) were randomly chosen from North China. Employees' facial photos were obtained from social networks and analyzed for happy, sad, and neutral emotion scores. Green spaces were analyzed as surface feature heights and area in 950m-buffer areas at every IP location. Green view index (GVI) was rated using a pre-trained machine-learning model on street view images (SVIs) crawled from the Baidu map. The Simpson diversity index was calculated by recognizing woody plant species in each SVI. RES was estimated as the difference of recruitment wage (mean ± standard deviation, 8625.62 ± 2735.54 CNY M-1) minus satisfactory salary (SS) (8153.77 ± 971.28 CNY M-1), which was positively impacted by GVI but a negative effect from Simpson plant diversity index. Although the green space area impaired happy score and perception of SS, it enforced a tiny contribution to RES with a negative contribution from the longitude of IPs.

Indexed as

EmotionsFacial ExpressionSalaries and Fringe BenefitsChinaHumans

Identifiers

PMID41575987
PMCPMC12829833

What OpenQuestion holds

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Registered trials

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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.