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Record W3140911851 · doi:10.21203/rs.3.rs-155206/v1

Satellite Imaging of Global Urbanicity relates to Brain and Behavior in Young People

2021· preprint· en· W3140911851 on OpenAlexaff
Günter Schumann, Jiayuan Xu, Xiaoxuan Liu, Alex Ing, Qiaojun Li, Wen Qin, Lining Guo, Conghong Huang, Jingliang Chen, Meiyun Wang, Zuojun Geng, Wenzhen Zhu, Bing Zhang, Weihua Liao, Shijun Qiu, Hui Zhang, Xiaojun Xu, Yongqiang Yu, Bo Gao, Tong Han, Guangbin Cui, Feng Chen, Junfang Xian, Jiance Li, Jing Zhang, Xi‐Nian Zuo, Dawei Wang, Wen Zen Shen, Yanwei Miao, Fei Yuan, Su Lui, Xiaochu Zhang, Kai Xü, Long Jiang Zhang, Zhaoxiang Ye, Tobias Banaschewski, Gareth J. Barker, Arun L.W. Bokde, Erin Burke Quinlan, Herta Flor, Antoine Grigis, Hugh Garavan, Penny Gowland, Andreas Heinz, Rüdiger Brühl, Jean‐Luc Martinot, Éric Artiges, Frauke Nees, Dimitri Papadopoulos Orfanos, Hervé Lemaître, Tomáš Paus, Luise Poustka, Sarah Hohmann, Juliane H. Fröhner, Michael N. Smolka, Henrik Walter, Robert Whelan, Ran Goldblatt, Kevin Patrick, Vince D. Calhoun, Mulin Lijun, Peng Gong, Edward D. Barker, Nicholas Clinton, Le Yu, Chunshui Yu, Qiang Luo, Huaigui Liu, Congying Chu, Liu Feng, IMAGEN Consortium, CHIMGEN Consortium

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldEnvironmental Science
TopicImpact of Light on Environment and Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSatelliteNeuroimagingRemote sensingPsychologyEnvironmental scienceGeographyNeuroscienceEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Abstract Urbanicity is a growing environmental challenge for mental-health. While the impact of urban life on brain and behavior might be distinct in different sociocultural conditions and geographies, there might exist features shared between regions. To investigate correlations of urbanicity with brain structure and function, neuropsychology and mental illness symptoms in young people from China and Europe, we developed a remote-sensing satellite-measure termed ‘UrbanSat’ quantifying population-density, a general measure of urbanicity. UrbanSat is correlated with brain volume, surface area and brain-network-connectivity in the medial prefrontal cortex and cerebellum, which mediate its effect on perspective-taking and depression- symptoms. Susceptibility to high population-density is greatest during childhood for the cerebellum and from childhood to adolescence for the prefrontal cortex. As UrbanSat can be generalized to different geographies, it will enable assessing the impact of urbanicity on mental illness and resilience globally, especially in young people where prevention and early interventions are most effective.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.367
Teacher spread0.338 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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