Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:57:07.955875Z
Paper Citation Record · LEDGER
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data
As of 9 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2506.04296.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
23 of 23 outbound references displayed
- verified exact5
- verified fuzzy6
- unresolved9
- parse uncertain0
- malformed identifier1
- metadata mismatch2
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation a586dd74-b49f-4a58-a835-c262ecd25d0f · outbound
This paper cites an unresolved cited work.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data Unresolved cited work
Reference 1
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T10:57:08.812090Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ce578c5c-654a-421e-a33d-7cdae85c5e18 · outbound
This paper cites Designed to remember long-term patterns, LSTMs are ideal for mining fleet data, where cyclic production schedules are common.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data Designed to remember long-term patterns, LSTMs are ideal for mining fleet data, where cyclic production schedules are common
Reference 2
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-07T10:57:08.789058Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 03925936-d95a-4f30-9345-2550630ed6c3 · outbound
This paper cites an unresolved cited work.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data Unresolved cited work
Reference 4
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T10:57:08.800603Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation afa144fd-bd0f-480f-8f79-3cdb0aae6de4 · outbound
This paper cites an unresolved cited work.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data Unresolved cited work
Reference 5
- Resolution
- unresolved
- raw_fallback, observed 2026-08-07T10:57:08.777299Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation de6fa76c-b244-42cb-98e1-69c537f9b55b · outbound
This paper cites FIG 7 – SHAP analysis: ‘upcoming rainfall’ feature impact on model predictions.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data FIG 7 – SHAP analysis: ‘upcoming rainfall’ feature impact on model predictions
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:57:08.742892Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 7f4eb02b-3344-45db-912e-b2e0a4d2cc48 · outbound
This paper cites XGBoost: Achieved a MedAE of 14.3 per cent.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data XGBoost: Achieved a MedAE of 14.3 per cent
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:57:08.732208Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c292be2b-80c4-4364-9004-bc8735a453d2 · outbound
This paper cites This analysis underscores the importance of integrating operational factors into predictive modelling.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data This analysis underscores the importance of integrating operational factors into predictive modelling
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:57:08.719307Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation c008c30f-f88c-41b7-ba6f-781bc659679d · outbound
This paper cites APCOM 2025 | Perth, Australia | 10–13 August 2025 15 Carvalho, M, Sampaio, P and Rebentisch, E, Carvalho, J.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data APCOM 2025 | Perth, Australia | 10–13 August 2025 15 Carvalho, M, Sampaio, P and Rebentisch, E, Carvalho, J
Reference 13
- Resolution
- verified exact
- raw_fallback, observed 2026-08-07T10:57:08.541762Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 1c25450a-5d37-477b-8733-c0be99b5ebcf · outbound
This paper cites https://doi.org/10.1080/17480930.2022.2142425 García, S, Luengo, J and Herrera, F,.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data https://doi.org/10.1080/17480930.2022.2142425 García, S, Luengo, J and Herrera, F,
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:57:07.924555Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7f5766c0-c35a-49f0-8ec1-cc1a26d61569 · outbound
This paper cites https://doi.org/10.1016/j.mineng.2023.108565 Soofastaei, A, Aminossadati, S, Kizil, M S and Knights, P,.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data https://doi.org/10.1016/j.mineng.2023.108565 Soofastaei, A, Aminossadati, S, Kizil, M S and Knights, P,
Reference 18
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-07T10:57:08.268286Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 0963992e-834e-405e-8695-199157295bf6 · outbound
This paper cites https://doi.org/10.1007/s10462-020-09838-1 Wang, Q, Zhang, R, Lv, S and Wang, Y,.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data https://doi.org/10.1007/s10462-020-09838-1 Wang, Q, Zhang, R, Lv, S and Wang, Y,
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:57:07.948620Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 794479ac-f9db-41b6-a954-875535896731 · outbound
This paper cites https://doi.org/10.1016/j.seta.2020.100977 Wang, W, Chakraborty, G and Chakraborty, B,.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data https://doi.org/10.1016/j.seta.2020.100977 Wang, W, Chakraborty, G and Chakraborty, B,
Reference 22
- Resolution
- metadata mismatch
- raw_fallback, observed 2026-08-07T10:57:08.206344Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation eb9fd360-b9fa-42ea-bc1d-c9afe67221fe · outbound
This paper cites https://doi.org/10.3390/app11010202 16 APCOM 2025 | Perth, Australia | 10–13 August 2025.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data https://doi.org/10.3390/app11010202 16 APCOM 2025 | Perth, Australia | 10–13 August 2025
Reference 23
- Resolution
- verified exact
- doi, observed 2026-08-07T10:57:07.987402Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 979460b6-4990-4f3b-9c9a-301dd28c399a · outbound
This paper cites https://doi.org/10.1108/17542731011085325 Baek, J and Choi, Y,.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data https://doi.org/10.1108/17542731011085325 Baek, J and Choi, Y,
Reference 2010
- Resolution
- verified exact
- doi, observed 2026-08-07T10:57:08.028409Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation ae0d8b93-1c73-4502-b57e-5f0479c38939 · outbound
This paper cites https://doi.org/10.1109/TCIAIG.2012.2186810 Cambitsis, A,.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data https://doi.org/10.1109/TCIAIG.2012.2186810 Cambitsis, A,
Reference 2012
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:57:07.913671Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7e5256f-11af-4548-b318-48b781472b5a · outbound
This paper cites Tlhatlhetji, M and Kolapo, P,.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data Tlhatlhetji, M and Kolapo, P,
Reference 2015
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:57:08.708983Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation e2e26d35-283c-4105-8d65-c8d94ec6da50 · outbound
This paper cites https://doi.org/10.1145/2939672.2939785 Fan, C, Zhang, N, Jiang, B and Liu, W V,.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data https://doi.org/10.1145/2939672.2939785 Fan, C, Zhang, N, Jiang, B and Liu, W V,
Reference 2016
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:57:07.920933Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a49b1e66-435d-4e14-b173-06f85c9a14cb · outbound
This paper cites https://doi.org/10.1007/s11053-018-9396-1 Hochreiter, S and Schmidhuber, J,.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data https://doi.org/10.1007/s11053-018-9396-1 Hochreiter, S and Schmidhuber, J,
Reference 2019
- Resolution
- verified exact
- doi, observed 2026-08-07T10:57:08.017649Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 94666ec1-d69b-4049-9e2c-c58b4049940d · outbound
This paper cites https://doi.org/10.1016/j.resourpol.2020.101569 Shimaponda-Nawa, M and Nwaila, G T,.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data https://doi.org/10.1016/j.resourpol.2020.101569 Shimaponda-Nawa, M and Nwaila, G T,
Reference 2020
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:57:07.932362Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dbc94778-ffef-43b1-a691-89498995e437 · outbound
This paper cites an unresolved cited work.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data Unresolved cited work
Reference 2021
- Resolution
- verified exact
- doi, observed 2026-08-07T10:57:08.006016Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 0be3375f-70e1-4b3c-886f-780d0656382c · outbound
This paper cites Gonzalez et al (2019) evaluated the effects of extreme rainfall events on open-pit mines in Peru, demonstrating marked operational delays during heavy rains.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data Gonzalez et al (2019) evaluated the effects of extreme rainfall events on open-pit mines in Peru, demonstrating marked operational delays during heavy rains
Reference 2022
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:57:08.753829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation f908e6b1-3130-4d5b-97c5-acf8d7d37d25 · outbound
This paper cites https://doi.org/10.23919/CCC58697.2023.10240705 Asif, M, Bessant, J and Francis, D,.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data https://doi.org/10.23919/CCC58697.2023.10240705 Asif, M, Bessant, J and Francis, D,
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:57:07.905934Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6856b4ad-82a2-447e-b8ec-aefff819df92 · outbound
This paper cites The region displays a clear seasonality, with a wet season spanning January to Mars, during which extreme events such as tropical depressions contribute to intense rainfall peaks.
Deep learning for predicting hauling fleet production capacity under uncertainties in open pit mines using real and simulated data The region displays a clear seasonality, with a wet season spanning January to Mars, during which extreme events such as tropical depressions contribute to intense rainfall peaks
Reference 2024
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-07T10:57:08.766374Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Pith citing papers
No inbound Pith citation observations are available.