Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-09T23:06:45.267976Z
Paper Citation Record · LEDGER
A temporal deep learning framework for calibration of low-cost air quality sensors
As of 3 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2604.21527.
A citation records a reference. It does not transfer a finding from one paper to another.
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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-03T06:30:56.289259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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Source: cited_works
Reference resolution
47 of 47 outbound references displayed
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f853d8f5-fba6-4c7d-b5ff-79ced70230ed · outbound
This paper cites Ambient (outdoor) air pollution.
A temporal deep learning framework for calibration of low-cost air quality sensors Ambient (outdoor) air pollution
Reference 1
- Resolution
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No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 1134d9c7-697c-468d-8b10-cba32858e3a0 · outbound
This paper cites Accessed: 2025.
A temporal deep learning framework for calibration of low-cost air quality sensors Accessed: 2025
Reference 2
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Source-reported events for the cited work
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Observation ae3e6dfd-1380-4d2f-913d-e2f69a219664 · outbound
This paper cites Global urban temporal trends in fine particulate matter (pm2·5) and attributable health burdens: estimates from global datasets.
A temporal deep learning framework for calibration of low-cost air quality sensors Global urban temporal trends in fine particulate matter (pm2·5) and attributable health burdens: estimates from global datasets
Reference 3
- Resolution
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Observation 87cc5d6c-b0c6-400d-8a52-10df43f3c2f9 · outbound
This paper cites Review of the performance of low-cost sensors for air quality monitoring.
A temporal deep learning framework for calibration of low-cost air quality sensors Review of the performance of low-cost sensors for air quality monitoring
Reference 4
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Source-reported events for the cited work
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Observation d84b9dc2-6a0a-467a-b4af-077cbe6f2345 · outbound
This paper cites Perspectives on the calibration and validation of low-cost air quality sensors.
A temporal deep learning framework for calibration of low-cost air quality sensors Perspectives on the calibration and validation of low-cost air quality sensors
Reference 5
- Resolution
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Observation 69e808dc-6287-412a-bdca-137a21f467eb · outbound
This paper cites Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges.
A temporal deep learning framework for calibration of low-cost air quality sensors Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges
Reference 6
- Resolution
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Observation 540ad3f6-c557-445b-8437-9f6224611bd2 · outbound
This paper cites In-field Calibration of Low-Cost Sensors through XGBoost $\&$ Aggregate Sensor Data.
A temporal deep learning framework for calibration of low-cost air quality sensors In-field Calibration of Low-Cost Sensors through XGBoost $\&$ Aggregate Sensor Data
Reference 7
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Source-reported events for the cited work
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Observation bbca0705-75dc-46f4-9d66-eafae21587a7 · outbound
This paper cites Challenges and opportunities in calibrating low-cost environmental sensors.
A temporal deep learning framework for calibration of low-cost air quality sensors Challenges and opportunities in calibrating low-cost environmental sensors
Reference 8
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Source-reported events for the cited work
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Observation 61fea357-b2cf-4f43-9177-ffa00b2352b5 · outbound
This paper cites On The Reliability Of Machine Learning Applications In Manufacturing Environments.
A temporal deep learning framework for calibration of low-cost air quality sensors On The Reliability Of Machine Learning Applications In Manufacturing Environments
Reference 9
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Source-reported events for the cited work
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Observation ef96258b-3984-457a-a44b-070d42a60da6 · outbound
This paper cites Blind calibration of air quality wireless sensor networks using deep neural networks.
A temporal deep learning framework for calibration of low-cost air quality sensors Blind calibration of air quality wireless sensor networks using deep neural networks
Reference 10
- Resolution
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Source-reported events for the cited work
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Observation 065c4f8d-d5c2-4401-9e92-c3e4a6a6b24d · outbound
This paper cites Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods.
A temporal deep learning framework for calibration of low-cost air quality sensors Evaluation and calibration of a low-cost particle sensor in ambient conditions using machine-learning methods
Reference 11
- Resolution
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Source-reported events for the cited work
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Observation 901b9391-7c73-4091-9ab2-6f673041fd4e · outbound
This paper cites Assessment and calibration of a low- cost pm2. 5 sensor using machine learning (hybridlstm neural network): Feasibility study to build an air quality monitoring system.
A temporal deep learning framework for calibration of low-cost air quality sensors Assessment and calibration of a low- cost pm2. 5 sensor using machine learning (hybridlstm neural network): Feasibility study to build an air quality monitoring system
Reference 12
- Resolution
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Source-reported events for the cited work
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Observation 8ce98306-6cfe-44fc-a426-bb8e473fb9bc · outbound
This paper cites Machine learning calib- ration of low-cost no 2 and pm 10 sensors: Non-linear algorithms and their impact on site transferability.
A temporal deep learning framework for calibration of low-cost air quality sensors Machine learning calib- ration of low-cost no 2 and pm 10 sensors: Non-linear algorithms and their impact on site transferability
Reference 13
- Resolution
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Source-reported events for the cited work
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Observation 083bef7b-e7c0-4a6b-bb41-fbea9ddb4cac · outbound
This paper cites A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring.
A temporal deep learning framework for calibration of low-cost air quality sensors A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring
Reference 14
- Resolution
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Source-reported events for the cited work
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Observation 8d42a507-491d-463f-ad57-291f1fac8a67 · outbound
This paper cites Field calibration of a low-cost air quality monitoring device in an urban background site using machine learning models.
A temporal deep learning framework for calibration of low-cost air quality sensors Field calibration of a low-cost air quality monitoring device in an urban background site using machine learning models
Reference 15
- Resolution
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Source-reported events for the cited work
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Observation 3632c9fd-dbc6-4354-8e96-478e99122492 · outbound
This paper cites Field calibration of a cluster of low-cost commercially available sensors for air quality monitoring. part b: No, co and co2.
A temporal deep learning framework for calibration of low-cost air quality sensors Field calibration of a cluster of low-cost commercially available sensors for air quality monitoring. part b: No, co and co2
Reference 16
- Resolution
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Observation 12cf9ff2-1c55-4a73-a691-a65da1d38f83 · outbound
This paper cites Machine Learning for Urban Air Quality Analytics: A Survey.
A temporal deep learning framework for calibration of low-cost air quality sensors Machine Learning for Urban Air Quality Analytics: A Survey
Reference 17
- Resolution
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Source-reported events for the cited work
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Observation 8da35804-83e3-40ad-a0b8-13dc80e10dce · outbound
This paper cites Dynamic calibration of low-cost pm2. 5 sensors using trust-based consensus mechanisms.
A temporal deep learning framework for calibration of low-cost air quality sensors Dynamic calibration of low-cost pm2. 5 sensors using trust-based consensus mechanisms
Reference 18
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 69265f39-971c-47d5-b509-05b31db99040 · outbound
This paper cites Performance of no, no 2 low cost sensors and three calibration approaches within a real world application.
A temporal deep learning framework for calibration of low-cost air quality sensors Performance of no, no 2 low cost sensors and three calibration approaches within a real world application
Reference 19
- Resolution
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Source-reported events for the cited work
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Observation 0b1fb343-09f1-4bf8-8eae-e623f927ac82 · outbound
This paper cites Machine learning techniques to improve the field performance of low-cost air quality sensors.
A temporal deep learning framework for calibration of low-cost air quality sensors Machine learning techniques to improve the field performance of low-cost air quality sensors
Reference 20
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation fede1128-6261-47e3-8352-213692e436b8 · outbound
This paper cites Deep spatio-temporal residual networks for city- wide crowd flows prediction.
A temporal deep learning framework for calibration of low-cost air quality sensors Deep spatio-temporal residual networks for city- wide crowd flows prediction
Reference 21
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Source-reported events for the cited work
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Observation fc39ece9-cc65-4f25-a862-64409eee5b81 · outbound
This paper cites Deep air quality forecasting using hybrid deep learning framework.
A temporal deep learning framework for calibration of low-cost air quality sensors Deep air quality forecasting using hybrid deep learning framework
Reference 22
- Resolution
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Source-reported events for the cited work
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Observation caaff5b9-e85f-4f7a-bb9e-76ed971505d2 · outbound
This paper cites Goodfellow, Y.
A temporal deep learning framework for calibration of low-cost air quality sensors Goodfellow, Y
Reference 23
- Resolution
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Source-reported events for the cited work
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Observation 7f676c80-12c7-40df-ad52-1a167340b271 · outbound
This paper cites Long short-term memory.
A temporal deep learning framework for calibration of low-cost air quality sensors Long short-term memory
Reference 24
- Resolution
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Source-reported events for the cited work
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Observation 76cd8625-d3f6-41b9-a561-64b9df6de859 · outbound
This paper cites Calibrations of low-cost air pollution monitoring sensors for co, no2, o3, and so2.
A temporal deep learning framework for calibration of low-cost air quality sensors Calibrations of low-cost air pollution monitoring sensors for co, no2, o3, and so2
Reference 25
- Resolution
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Source-reported events for the cited work
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Observation fce0e555-2437-4f66-91ae-f9931dcc4833 · outbound
This paper cites Band-sensitive calibration of low-cost pm2. 5 sensors by lstm model with dynamically weighted loss function.
A temporal deep learning framework for calibration of low-cost air quality sensors Band-sensitive calibration of low-cost pm2. 5 sensors by lstm model with dynamically weighted loss function
Reference 26
- Resolution
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Source-reported events for the cited work
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Observation a90a0204-350d-4fa8-bd04-8e1878662c51 · outbound
This paper cites Few-shot calibration of low-cost air pollution (pm _{2.5}) sensors using meta learning.
A temporal deep learning framework for calibration of low-cost air quality sensors Few-shot calibration of low-cost air pollution (pm _{2.5}) sensors using meta learning
Reference 27
- Resolution
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Source-reported events for the cited work
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Observation 40c5faf0-9404-43f3-a923-7920df1964b7 · outbound
This paper cites Developing a relative humidity correction for low-cost sensors measuring ambient particulate matter.
A temporal deep learning framework for calibration of low-cost air quality sensors Developing a relative humidity correction for low-cost sensors measuring ambient particulate matter
Reference 28
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Observation fb7e8825-cb1f-4821-84e2-655e8f4af8a3 · outbound
This paper cites Towards a hygroscopic growth calibration for low-cost pm 2.5 sensors.
A temporal deep learning framework for calibration of low-cost air quality sensors Towards a hygroscopic growth calibration for low-cost pm 2.5 sensors
Reference 29
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This paper cites Performance as- sessment of low-and medium-cost pm2. 5 sensors in real-world conditions in central europe.
A temporal deep learning framework for calibration of low-cost air quality sensors Performance as- sessment of low-and medium-cost pm2. 5 sensors in real-world conditions in central europe
Reference 30
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Observation e3722b7e-7e03-4db5-9334-60579950b62f · outbound
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A temporal deep learning framework for calibration of low-cost air quality sensors Iot based air pollution monitoring & prediction system
Reference 31
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Observation 20cc1054-9f22-4783-8c8b-bac970eca69c · outbound
This paper cites Deep learning architecture for air quality predictions.
A temporal deep learning framework for calibration of low-cost air quality sensors Deep learning architecture for air quality predictions
Reference 32
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Observation 8c6aa1a4-83f6-4b8f-9aaf-41ba542ac109 · outbound
This paper cites Sensor based ambient air concen- tration data for nitrogen dioxide and particles in oxford, measured by the oxaria project 2020 to 2021.
A temporal deep learning framework for calibration of low-cost air quality sensors Sensor based ambient air concen- tration data for nitrogen dioxide and particles in oxford, measured by the oxaria project 2020 to 2021
Reference 33
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Observation 4104690b-b6d1-49b7-9f42-901974e297cd · outbound
This paper cites Can commercial low-cost sensor platforms contribute to air qual- itymonitoringandexposureestimates?.
A temporal deep learning framework for calibration of low-cost air quality sensors Can commercial low-cost sensor platforms contribute to air qual- itymonitoringandexposureestimates?
Reference 34
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A temporal deep learning framework for calibration of low-cost air quality sensors Recursive and rolling windows for medical time series forecasting: a comparative study
Reference 35
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A temporal deep learning framework for calibration of low-cost air quality sensors Rolling window time series prediction using mapreduce
Reference 36
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A temporal deep learning framework for calibration of low-cost air quality sensors Random search for hyper-parameter optimization
Reference 37
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A temporal deep learning framework for calibration of low-cost air quality sensors Dro- pout: a simple way to prevent neural networks from overfitting
Reference 38
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This paper cites Assessment of the performance of a low-cost air quality monitor in an indoor environment through different calibration models.
A temporal deep learning framework for calibration of low-cost air quality sensors Assessment of the performance of a low-cost air quality monitor in an indoor environment through different calibration models
Reference 39
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A temporal deep learning framework for calibration of low-cost air quality sensors Array program- ming with numpy
Reference 40
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Source-reported events for the cited work
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A temporal deep learning framework for calibration of low-cost air quality sensors Guide to the demonstration of equivalence of ambient air monitoring methods
Reference 41
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Source-reported events for the cited work
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Observation edca61eb-3a9d-40e6-a2d7-94e770d6f012 · outbound
This paper cites Directive 2008/50/ec of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe.
A temporal deep learning framework for calibration of low-cost air quality sensors Directive 2008/50/ec of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe
Reference 42
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This paper cites Scikit-learn: Machine learning in python.
A temporal deep learning framework for calibration of low-cost air quality sensors Scikit-learn: Machine learning in python
Reference 43
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 5e12ac0f-8dc2-4bb3-bbeb-da5b5090bf76 · outbound
This paper cites Modelflows-app: data-driven post- processing and reduced order modelling tools.
A temporal deep learning framework for calibration of low-cost air quality sensors Modelflows-app: data-driven post- processing and reduced order modelling tools
Reference 44
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Source-reported events for the cited work
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A temporal deep learning framework for calibration of low-cost air quality sensors Hierarchical higher- order dynamic mode decomposition for clustering and feature selection
Reference 45
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A temporal deep learning framework for calibration of low-cost air quality sensors Data splitting technique to fit any machine learning model
Reference 46
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Source-reported events for the cited work
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Observation bbc787c0-d0eb-463a-b222-4421aa6f79e4 · outbound
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A temporal deep learning framework for calibration of low-cost air quality sensors Ideal dataset splitting ratios in machine learning algorithms: General concerns for data scientists and data analysts
Reference 47
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Pith citing papers
No inbound Pith citation observations are available.