{"id":"4a24c9cd-cc6e-4108-9bf3-ff307ae50ce8","arxiv_id":"2606.19539","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":2.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"A review of ML models for SEP prediction that compares architectures, datasets, inputs and outputs while recommending good practices for future work.","lead":"This paper reviews machine learning models for predicting solar energetic particle events from the sun. A smart generalist might read it to understand current forecasting tools for space weather risks to satellites and astronauts.","discovery_kind":"unclear","skeptic_critique":{"model":"grok-4.3","headline":"Whether published ML SEP studies are mature/comparable enough for cross-model evaluation and reliable best-practice extraction","rationale":"The reader's weakest_assumption directly names the condition required for the review's central claim to deliver reliable insights. No other internal inconsistency or hidden assumption is visible from the purpose statement; the low-confidence UNVERDICTED stance already accounts for the provisional nature of the assessment.","tokens_in":1670,"tokens_out":291,"duration_ms":11015,"concrete_test":"From the papers cited in the review, tabulate the fraction that share at least one common dataset and use the same primary metric (e.g., skill score or event-based POD/FAR); if <40% overlap on both, recompute any 'best practice' claims after restricting to the comparable subset and check whether conclusions change.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The manuscript's stated purpose (abstract) is to review ML models, identify datasets, compare architectures/inputs/outputs, and derive good practices/recommendations. This requires the underlying literature to be sufficiently standardized in data sources, metrics, validation approaches, and prediction targets so that comparisons are meaningful rather than apples-to-oranges. If most studies use incompatible setups or limited validation, the extracted recommendations rest on shaky ground. This is precisely the reader's weakest_assumption and is load-bearing for any review claiming to synthesize best practices.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript reviews currently available machine learning models for solar energetic particle (SEP) prediction. It identifies the datasets used for training, compares their architectures, inputs, and outputs, and outlines good practices and recommendations for future research based on these insights.","tokens_in":1761,"tokens_out":227,"duration_ms":18231,"significance":"If the underlying literature permits meaningful synthesis, the review could help standardize approaches in an emerging application of ML to space weather, potentially improving prediction reliability and guiding efficient use of observational datasets for radiation hazard mitigation.","major_comments":[{"comment":"Abstract (purpose statement): the claim that good practices and recommendations can be reliably extracted rests on the assumption that published ML SEP studies are sufficiently standardized in datasets, metrics, validation approaches, and prediction targets. The manuscript must explicitly assess and document heterogeneity across the reviewed papers (e.g., in a dedicated comparison table or subsection) to justify any synthesized recommendations; without this, the central descriptive and prescriptive claims cannot be verified as robust.","section":null}],"minor_comments":[],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive comment highlighting the need to explicitly document heterogeneity to support synthesized recommendations. We address the point below.","responses":[{"response":"We agree that an explicit assessment of heterogeneity strengthens the justification for recommendations. The manuscript already performs comparisons of architectures, datasets, inputs, and outputs across studies, which inherently surfaces variations in these elements. To make the heterogeneity fully transparent and directly support the prescriptive claims, we will add a dedicated subsection together with a summary comparison table that systematically tabulates differences in datasets, metrics, validation approaches, and prediction targets. This revision will allow readers to evaluate the robustness of the extracted good practices.","revision_made":"yes","referee_comment":"Abstract (purpose statement): the claim that good practices and recommendations can be reliably extracted rests on the assumption that published ML SEP studies are sufficiently standardized in datasets, metrics, validation approaches, and prediction targets. The manuscript must explicitly assess and document heterogeneity across the reviewed papers (e.g., in a dedicated comparison table or subsection) to justify any synthesized recommendations; without this, the central descriptive and prescriptive claims cannot be verified as robust."}],"tokens_in":1174,"tokens_out":253,"duration_ms":20068,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"This manuscript is a review that collects machine learning models for forecasting solar energetic particle events. It catalogs the datasets used in prior papers, compares model architectures along with their inputs and outputs, and extracts some recommendations for future work.\n\nThe paper does a straightforward job of framing the shift from physics-based and empirical methods to ML approaches. It also ties the topic to real stakes like radiation risks for spacecraft and aviation, which helps set context without overclaiming.\n\nThe main soft spot is the load-bearing assumption that the published ML studies are similar enough in data sources, event definitions, metrics, and validation schemes to allow reliable comparisons and best-practice extraction. SEP forecasting papers frequently differ on these points, so any synthesis risks mixing incompatible results. The abstract gives no sign that this heterogeneity was systematically addressed, which leaves the recommendations on uncertain ground.\n\nThe work is aimed at researchers in heliophysics or space weather who want a single entry point into the ML literature on SEPs. Specialists already familiar with the cited papers will find little new, while newcomers or people from adjacent fields could use the overview.\n\nIt deserves a serious referee. A review that organizes a scattered subfield can be useful if the comparisons are careful and the limitations around comparability are stated plainly. I would send it to peer review with instructions to the referee to check whether the cross-study evaluation holds up given the likely differences in the source material.","headline":"This review maps ML work on SEP prediction but its value hinges on whether the source studies are comparable enough to support cross-model recommendations.","tokens_in":2635,"tokens_out":354,"would_cite":false,"duration_ms":19355,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Review of machine learning models for solar energetic particle prediction compares datasets, architectures, inputs and outputs to extract good practices.","keywords":["solar energetic particles","machine learning","SEP prediction","space weather","neural network architectures","training datasets","best practices","radiation hazards"],"falsifier":"A controlled experiment in which new SEP prediction models built with and without the review's recommended practices are evaluated on the same independent test set of events, showing no measurable difference in forecast skill, would falsify the utility of the extracted practices.","tokens_in":2589,"feed_emoji":"☀️","tokens_out":434,"duration_ms":21810,"temperature":0.7,"pith_summary":"This paper reviews the machine learning models developed for predicting solar energetic particle events. It catalogs the datasets used to train those models and compares their architectures along with the inputs they receive and the outputs they generate. From these comparisons the authors synthesize insights and propose good practices for future work in the area. Readers should care because SEP events produce radiation hazards for aviation, spacecraft electronics and crewed missions beyond Earth orbit. Consolidating an emerging set of data-driven approaches alongside traditional physics-based methods can support more reliable forecasts that protect technology and exploration.","feed_headline":"Review compares ML models for solar energetic particle prediction","feed_subtitle":"Catalog of datasets and architectures yields recommendations to guide more consistent future forecasts of radiation events.","key_machinery":"Systematic comparison across published ML models of their training datasets, neural network architectures, input features from solar and heliospheric observations, and output predictions of SEP event properties.","core_discovery":"The purpose of this manuscript is to review the currently available ML models for SEP prediction, identify the datasets used for training, compare their architectures, inputs, and outputs, and, based on these insights, outline good practices and recommendations for future research.","pith_inferences":[],"forward_implications":[],"fun_headline_variants":["ML models for SEP prediction compared with datasets reviewed","Architectures and inputs surveyed for solar energetic particle ML","SEP forecasting ML review catalogs models and recommendations","Machine learning approaches to SEP prediction compared in survey"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"That the body of published ML studies for SEP prediction is sufficiently mature and comparable across papers to allow meaningful cross-model evaluation and extraction of reliable best practices.","fun_headline_variants_meta":{"raw":{"variants":["ML models for SEP prediction compared with datasets reviewed","Architectures and inputs surveyed for solar energetic particle ML","SEP forecasting ML review catalogs models and recommendations","Machine learning approaches to SEP prediction compared in survey"]},"model":"grok-4.3","cost_usd":0.004772,"raw_usage":{"total_tokens":2306,"prompt_tokens":579,"num_sources_used":0,"completion_tokens":57,"cost_in_usd_ticks":47724500,"prompt_tokens_details":{"text_tokens":579,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1670,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":579,"tokens_out":57,"duration_ms":12765,"temperature":1.0,"reasoning_tokens":1670,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-26T18:53:11.563217+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A controlled experiment in which new SEP prediction models built with and without the review's recommended practices are evaluated on the same independent test set of events, showing no measurable difference in forecast skill, would falsify the utility of the extracted practices.","supporting_citations":[],"review_version":1}