{"id":"W2525492182","doi":"","title":"A new perspective: Atlantic herring (Clupea harengus) as a case study for time series analysis and historical data","year":2008,"lang":"en","type":"article","venue":"University of New Hampshire Scholars Repository (University of New Hampshire at Manchester)","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Clupea; Atlantic herring; Herring; Series (stratigraphy); Fishery; Time series; Perspective (graphical); Geography; Environmental science; Computer science; Geology; Artificial intelligence; Fish <Actinopterygii>; Biology; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005557573,0.0004950468,0.0005771695,0.003416354,0.001569447,0.00531497,0.0009316786,0.001703175,0.00167896],"category_scores_gemma":[0.008156376,0.0001854732,0.0006647349,0.00528184,0.001981869,0.004379209,0.001515114,0.002446934,0.0002857023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001681116,"about_ca_system_score_gemma":0.001345548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0126404,"about_ca_topic_score_gemma":0.02678506,"domain_scores_codex":[0.9969732,0.00202864,0.0001710344,0.0002146372,0.0004915202,0.0001209611],"domain_scores_gemma":[0.9906845,0.006828421,0.0006547183,0.0004408327,0.0009713306,0.0004201211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001973702,0.0004334922,0.1017274,0.002046951,0.0002993496,0.01930214,0.06046978,0.009569969,0.007190703,0.4863115,0.04024618,0.2722052],"study_design_scores_gemma":[0.00002976089,0.0005858751,0.07001871,0.004185914,0.0002138115,0.01014512,0.1268989,0.04456495,0.002525175,0.09986284,0.6407048,0.000264227],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4489994,0.1079214,0.2111295,0.0942645,0.004154516,0.0004198768,0.002016677,0.0002639135,0.1308302],"genre_scores_gemma":[0.7247458,0.05028292,0.1955341,0.004045232,0.00273043,0.0004116778,0.001065648,0.0001891257,0.02099514],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0126404,"threshold_uncertainty_score":0.02939159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03801991300605729,"score_gpt":0.237457021195259,"score_spread":0.1994371081892017,"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."}}