{"id":"W4411361814","doi":"10.20944/preprints202506.1365.v1","title":"Reanalyzing and Reinterpreting a Unique Set of Antarctic Acoustic Frazil Data Using River Frazil Results and Self-Validating 2-Frequency Analyses","year":2025,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"ASL Environmental Sciences (Canada)","funders":"","keywords":"Data set; Geology; Set (abstract data type); Acoustics; Environmental science; Climatology; Physics; Computer science; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.003447657,0.0005106039,0.0008453373,0.0006301901,0.0003402325,0.0001630324,0.00151563,0.0003974951,0.000276996],"category_scores_gemma":[0.002582175,0.000495852,0.00009475715,0.0004758705,0.0004405817,0.0004239888,0.003885253,0.001422228,0.00001463823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005912978,"about_ca_system_score_gemma":0.00058654,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03644351,"about_ca_topic_score_gemma":0.001858518,"domain_scores_codex":[0.994769,0.0007747748,0.001166894,0.001923351,0.0007116735,0.0006543457],"domain_scores_gemma":[0.9951568,0.001337646,0.0006744782,0.002240267,0.0003238997,0.0002669418],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001112868,0.00003151916,0.906209,0.001802882,0.0005845499,0.00007132044,0.003173427,0.07866048,0.00836705,0.000001989952,0.000008719075,0.0009777967],"study_design_scores_gemma":[0.0004587605,0.00004213013,0.1350179,0.001917537,0.0007005117,0.00004321552,0.0005148334,0.8548989,0.004039918,0.001756428,0.00002999676,0.0005797808],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9819735,0.0006787826,0.01156809,0.00009669287,0.0001772146,0.0006177485,0.002006399,0.0001204191,0.00276114],"genre_scores_gemma":[0.9689255,0.001148459,0.02890091,0.00003651325,0.0001069383,0.000002757433,0.0007738254,0.00001738795,0.00008770287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7762384,"threshold_uncertainty_score":0.9997493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2632890452807936,"score_gpt":0.4225925896521369,"score_spread":0.1593035443713433,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}