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Good Space a ranking of the record

TheGood surrounding AI

100 gains match this view. 94% rest on at least one peer-reviewed study, and 17% clear the high-evidence bar. The latest was filed Oct 16, 2026.

Every gain in this view, one square each, shaded by the strength of its evidence. High 17 Moderate 83 Emerging 0
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Sector: science

Matching records · page 1 of 4

2
Evidence-backed gainPeer-reviewedScience

Generative AI offers faster and scaled-up foresight that can serve as anticipatory infrastructure for imagining possible futures and foresight solutions.

Source article: ARTIFICIAL INTELLIGENCE AND THE FUTURES TURN: an anticipatory infrastructure for qualitative methods

Futures · European Business Organization Law Review
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3
Evidence-backed gainPeer-reviewedClimate

Panel analysis of 67 countries found AI development significantly reduces ecological footprints and carbon emissions while promoting energy transitions, with the largest effect on energy transitions.

Source article: Ecological footprints, carbon emissions, and energy transitions: the impact of artificial intelligence (AI)

Journal of Electronic Business & Digital Economics · Sustainability
Reader signal

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5
Evidence-backed gainPeer-reviewedClimate

AI enables real-time pollution detection, air and water quality monitoring, and accurate disaster and air quality forecasting to support prompt interventions and sustainable urban management.

Source article: Artificial intelligence in environmental monitoring: Advancements, challenges, and future directions

Hygiene and Environmental Health Advances · Frontiers in Environmental Science
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6
Evidence-backed gainPeer-reviewedScience

A three-track neural network that jointly processes sequence, distance, and coordinate information enables accurate prediction of protein structures and protein-protein complexes, approaching DeepMind's accuracy.

Source article: Accurate prediction of protein structures and interactions using a three-track neural network

Science · Annual Review of Biophysics
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8
Evidence-backed gainPeer-reviewedScience

Decision-tree ensemble approach called deep forest achieves excellent performance across different domains using the same default setting while requiring fewer hyper-parameters and automatically determining model complexity without gradient-based adjustment or backpropagation.

Source article: Deep forest

Water Resources Research · Journal of Network and Computer Applications
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9
Evidence-backed gainPeer-reviewedScience

Combining machine learning with multiscale modeling creates robust predictive models that integrate underlying physics to manage ill-posed problems and can provide insights into disease mechanisms and treatment strategies.

Source article: Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

npj Digital Medicine · Informatics
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10
Evidence-backed gainPeer-reviewedScience

AlphaFold provides a computational method that regularly predicts protein three-dimensional structures with atomic accuracy from amino acid sequence alone, even without homologous structures.

Source article: Highly accurate protein structure prediction with AlphaFold

Nature · International Journal of Molecular Sciences
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11
Evidence-backed gainPeer-reviewedClimate

AI readiness was associated with improved national sustainability performance across G20 economies from 2015-2023, with the strongest effect among the three technologies studied.

Source article: Digital Technologies and Sustainable Development: Evidence from FinTech, AI, and Blockchain Adoption in G20 Economies

Research Journal of Human and Social Aspects · Journal of Global Social Transformation
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12
Evidence-backed gainPeer-reviewed ×2Science

AI-powered voice assistants were found to enhance independence and daily functioning and offer emotional companionship for socially isolated older adults

Source article: AI-powered voice assistants for older adults: a literature review of insights, research practices, and future directions

Aston Publications Explorer (Aston University) · Technological Forecasting and Social Change
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13
Evidence-backed gainPeer-reviewedScience

In the past decade, the global industry and research attentions on intelligent skin-like electronics have boosted their applications in diverse fields including human healthcare, Internet of Things, human-machine interfaces, artificial intelligence and soft robotics.

Source article: Multifunctional Flexible Humidity Sensor Systems Towards Noncontact Wearable Electronics

Nano-Micro Letters · Artificial Intelligence Review
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14
Evidence-backed gainPeer-reviewedScience

The development and release of ethics guidelines provides normative principles intended to help harness disruptive potentials of AI systems in research, development and application.

Source article: The Ethics of AI Ethics: An Evaluation of Guidelines

Minds and Machines · International Journal of Information Management
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15
Evidence-backed gainPeer-reviewedScience

ChatMOF enables researchers to retrieve, predict, and generate metal-organic frameworks from natural language queries with high accuracy using GPT-4.

Source article: ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models

Advanced Functional Materials · Journal of the American Chemical Society
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16
Evidence-backed gainPeer-reviewedScience

A deep neural network trained on DFT data learned a transferable potential that predicts total energies for organic molecules with chemical accuracy versus DFT, generalizing to systems up to 54 atoms despite training on smaller molecules.

Source article: ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

Nature · Physical Review Research
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18
Evidence-backed gainPeer-reviewedScience

AI-based hand-motion tracking let researchers build Operation Profiles and measure how closely trainees matched an expert's technique, with feedback linked to improving similarity.

Source article: Ambient intelligence-based framework for quantitative evaluation of cell culture operations to support standardization and training

Regenerative Therapy
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19
Evidence-backed gainPeer-reviewedClimate

By using physicochemical properties as design variables, the AI framework found a property combination that, relative to diesel, raised thermal efficiency and cut NOx and soot emissions in a proof-of-concept test.

Source article: An AI-driven framework for property-oriented fuel design toward sustainable engine combustion

iScience
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23
Evidence-backed gainPeer-reviewedScience

Combining a convolutional and a temporal convolutional network lifted ion classification accuracy by 25.9% over an unmodified CNN and beat LSTM variants and traditional machine learning in a microfluidic electrochemical sensing platform.

Source article: A spatial-temporal deep learning method for heavy metal ion detection

The Analyst
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24
Evidence-backed gainPeer-reviewedScience

Language models can pull interaction types and participating species out of multilingual citizen science comments with high accuracy, which could widen the coverage of scarce ecological interaction data.

Source article: Large language models unlock the ecology of species interactions

Proceedings of the National Academy of Sciences
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26
Evidence-backed gainPeer-reviewedScience

Combining linked-segment physics with a BiLSTM network estimated vertical ground reaction forces from seven wearable IMUs with errors below a high-accuracy reference in intra- and inter-subject tests.

Source article: Load-Decomposition Learning Enhances Ground Reaction Force Estimation Using IMUs

IEEE Transactions on Neural Systems and Rehabilitation Engineering
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27
Evidence-backed gainPeer-reviewedClimate

A machine learning classifier applied to multi-sensor satellite and terrain data produced a consistent circumpolar wetland baseline that can improve methane flux models and conservation planning.

Source article: PAWI: The first high-resolution, multi-class, Pan-Arctic Wetland Inventory

Journal of Environmental Management
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28
Evidence-backed gainPeer-reviewedScience

Adding an Inspector agent that checks each proposal against nine physical constraints increased the share of generated constitutive models passing all checks, from 90% to 95% for Opus and from 47% to 60% for Kimi.

Source article: LLM-driven design of physics-constrained constitutive models: Two agents are better than one

Machine Learning for Computational Science and Engineering
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29
Evidence-backed gainPeer-reviewedClimate

A machine learning model using sensor, soil, nitrogen and free climate data predicted wheat yield before nitrogen application with R² of 0.81, versus 0.12 for the INSEY benchmark.

Source article: Wheat yield prediction using machine learning, proximal sensing, nitrogen and climate data for Oklahoma

Precision agriculture
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30
Evidence-backed gainPeer-reviewedScience

Pairing amino acid and 3Di structural alignments in generative landscapes recovered known subfamilies and revealed evolutionary relationships in viral glycoproteins that sequence alone did not show.

Source article: A structure-aware generative AI framework for revealing functional relationships in protein families

Proceedings of the National Academy of Sciences
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