{"id":"W3120136228","doi":"10.5267/j.dsl.2020.11.002","title":"Determinant factors of fishermen income and decision-making for providing welfare insurance: An application of multinomial logistic regression","year":2021,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Marine and Coastal Ecosystems","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universitas Padjadjaran; Universiti Malaysia Terengganu","keywords":"Multinomial logistic regression; Welfare; Poverty; Fishing; Business; Socioeconomic status; Logistic regression; Household income; Socioeconomics; Geography; Fishery; Economics; Economic growth; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006641559,0.000123158,0.0002315387,0.00009944009,0.0002553338,0.00005375054,0.0004084653,0.00004681411,0.00003589578],"category_scores_gemma":[0.0006148111,0.00009300433,0.00004930196,0.0005330893,0.0003479406,0.0005339613,0.0004527652,0.00005923056,0.000001566774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007792954,"about_ca_system_score_gemma":0.00002084384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001189303,"about_ca_topic_score_gemma":0.0001188101,"domain_scores_codex":[0.9981788,0.00002661096,0.0004413422,0.0005253189,0.0006045669,0.0002233089],"domain_scores_gemma":[0.9987813,0.0003839871,0.0002963238,0.0004012753,0.00004724271,0.0000898194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005837395,0.00003326454,0.3425295,0.00002395417,9.720075e-7,0.000003366035,0.0002211217,0.0005089387,0.2023882,0.00001728197,0.00001386707,0.4542012],"study_design_scores_gemma":[0.0004985824,0.0001198451,0.9387442,0.000223275,0.00000763438,0.00001580903,0.0004514704,0.03440795,0.02420326,0.0007072588,0.0004091782,0.0002115285],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9538082,0.00000667558,0.04560617,0.00006086777,0.0001340433,0.0003018345,0.00001680357,0.000008762743,0.00005664165],"genre_scores_gemma":[0.9841385,0.000002712329,0.01575949,0.00005449666,0.00001362235,0.00001815269,0.000002864395,0.000007542856,0.000002541933],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5962147,"threshold_uncertainty_score":0.3792607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0175692132429347,"score_gpt":0.2947091853678068,"score_spread":0.2771399721248721,"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."}}