Scientific Paper · source-verified

A Machine Learning Model Using The Snapshot Ensemble Approach For Soil Respiration Prediction In An Experimental Oak Forest

Syeda Nyma Ferdous; Jayendra Pandit Ahire; Richard Bergman; Xin Li; Elena Blanc-Betes; Zhou Zhang; Jinxin Wang · 2025-01-08

A hybrid machine learning model improves soil respiration predictions, enhancing carbon cycle modeling and greenhouse gas management strategies.

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Syeda Nyma Ferdous; Jayendra Pandit Ahire; Richard Bergman; Xin Li; Elena Blanc-Betes; Zhou Zhang; Jinxin Wang. 2025-01-08. A Machine Learning Model Using The Snapshot Ensemble Approach For Soil Respiration Prediction In An Experimental Oak Forest. https://www.sciencedirect.com/science/article/pii/S1574954124005338 (AI & Environment Resource Hub; record paper-110; collection snapshot 2026-09-15).

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Author or creator
Syeda Nyma Ferdous; Jayendra Pandit Ahire; Richard Bergman; Xin Li; Elena Blanc-Betes; Zhou Zhang; Jinxin Wang
Publisher
ScienceDirect
Publication date
2025-01-08
Date precision
day
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Unknown
Language
unknown
Peer review
unknown
Source status
verified
Last verified
2026-09-15T16:58:06Z
Snapshot import
2026-09-06
Legacy domain
Biodiversity & Ecosystems
Legacy subdomain
Modeling Soil Respiration Prediction in An Experimental Oak Forest
journal
ScienceDirect

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Source sheet: Scientific Paper · Row 114 · Original ID: paper-110.

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A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-15. Source identity and required metadata verified. The import date is not the original date added.

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Shared topics: Climate and greenhouse gases; Biodiversity and ecosystems; Agriculture and food

Scientific Paper · 2025-09-24

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Introduces BoreaRL, a multi-objective reinforcement learning environment for climate-adaptive boreal forest management. Boreal forests store 30-40% of terrestrial carbon, much in climate-vulnerable permafrost soils. The authors show carbon sequestration goals are easier to optimize than permafrost preservation, and that effective policies must balance species composition and density to protect permafrost while maintaining carbon gains.

arXiv

Shared topics: Climate and greenhouse gases; Biodiversity and ecosystems; Agriculture and food

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EarthMap

Earth Map is an innovative, free and open-source tool developed by the Food and Agriculture Organization of the United Nations (FAO) in the framework of the FAO - Google partnership. It was created to support countries, research institutes, farmers and members of the general public with internet access to monitor their land in an easy, integrated and multi-temporal manner. It allows everyone to visualize, process and analyze satellite imagery and global datasets on climate, vegetation, fires, biodiversity, geo-social and other topics. Users need no prior knowledge of remote sensing or Geographical Information Systems (GIS).

Food and Agriculture Organization of the United Nations (FAO)free

Shared topics: Climate and greenhouse gases; Biodiversity and ecosystems; Agriculture and food

Shared topics: Climate and greenhouse gases; Biodiversity and ecosystems; Agriculture and food