{"id":"W1998765045","doi":"10.1017/s0373463303002285","title":"A New Approach to Sequential Tidal Prediction","year":2003,"lang":"en","type":"article","venue":"Journal of Navigation","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Tide gauge; Artificial neural network; Tidal Model; Feedforward neural network; Tidal power; Least-squares function approximation; Computer science; Algorithm; Geology; Engineering; Artificial intelligence; Marine engineering; Statistics; Mathematics; Oceanography; Sea level","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009870999,0.0009133014,0.0009553646,0.0007590229,0.0004837428,0.000978482,0.00185847,0.0007304526,0.003502302],"category_scores_gemma":[0.00297253,0.0005077934,0.0007842245,0.00118163,0.0006338547,0.001983644,0.001294332,0.001640062,0.0008997662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000618367,"about_ca_system_score_gemma":0.00143233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007428826,"about_ca_topic_score_gemma":0.007561977,"domain_scores_codex":[0.9990214,0.0001385714,0.00006875431,0.0002996722,0.0004089032,0.00006275118],"domain_scores_gemma":[0.9988804,0.0003763165,0.0001183212,0.0002031473,0.0003714385,0.00005027492],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001032794,0.0001369713,0.002188572,0.0001646305,0.0001359674,0.0002007986,0.0001965242,0.5740136,0.006176266,0.07233041,0.004793711,0.3395593],"study_design_scores_gemma":[0.000005535807,0.00002812251,0.0001315476,0.000006610661,0.00001227232,0.00003270822,0.000006029848,0.9839808,0.0006606039,0.01206799,0.003058416,0.00000933305],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002205715,0.00009338577,0.9956549,0.00009735462,0.00008575594,0.00002494025,0.00005003798,0.0002541216,0.001533658],"genre_scores_gemma":[0.2766756,0.0007063819,0.7083283,0.0002588884,0.0004008416,0.0003105564,0.0004986552,0.0002154731,0.01260542],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007428826,"threshold_uncertainty_score":0.01477116,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02779827287762054,"score_gpt":0.2566319178573187,"score_spread":0.2288336449796982,"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."}}