{"id":"W2039242059","doi":"10.1016/j.ecoinf.2008.04.001","title":"A quantitative approach for classifying fish otolith strontium: Calcium sequences into environmental histories","year":2008,"lang":"en","type":"article","venue":"Ecological Informatics","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère des Ressources Naturelles et de la Faune","keywords":"Otolith; Brackish water; Fish migration; Strontium; Zoning; Algorithm; Freshwater fish; Transect; Fishery; Ecology; Fish <Actinopterygii>; Biology; Computer science; Chemistry; Engineering; Salinity","routes":{"ca_aff":true,"ca_fund":true,"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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002572504,0.0001712779,0.0002389931,0.00002938316,0.0004999361,0.00004007852,0.0003399488,0.0001324572,0.002330371],"category_scores_gemma":[0.000131237,0.0001311659,0.000087645,0.0001205801,0.0009684982,0.0005680606,0.0003632422,0.000225306,0.0000710681],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004843999,"about_ca_system_score_gemma":0.0000250962,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008792826,"about_ca_topic_score_gemma":0.0000384255,"domain_scores_codex":[0.9985581,0.00004107493,0.0004167018,0.000170376,0.0003697969,0.0004439388],"domain_scores_gemma":[0.9993818,0.0001564417,0.0001292146,0.0001746714,0.000008024045,0.0001498455],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005937747,0.001766702,0.821404,0.0003754331,0.0001582219,0.00004079139,0.04509873,0.004639082,0.0007156233,0.004839864,0.1076334,0.01273431],"study_design_scores_gemma":[0.001878482,0.00383602,0.1266172,0.000005162425,0.00003860642,0.00007784439,0.02603933,0.2361795,0.0004604047,0.001425427,0.6021443,0.001297779],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6717287,0.00000782951,0.05081694,0.0002770117,0.0001399245,0.001100184,0.00006707781,0.00009637058,0.275766],"genre_scores_gemma":[0.8518654,0.00004381732,0.1439129,0.000571967,0.00003925052,0.0002697642,0.0001371473,0.00001165379,0.003148078],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6947868,"threshold_uncertainty_score":0.9985816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07711593307584694,"score_gpt":0.2870210579445844,"score_spread":0.2099051248687374,"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."}}