{"id":"W4313396357","doi":"10.3389/fmars.2022.954959","title":"A will-o’-the wisp? On the utility of voluntary contributions of data and knowledge from the fishing industry to marine science","year":2022,"lang":"en","type":"article","venue":"Frontiers in Marine Science","topic":"Risk Perception and Management","field":"Social Sciences","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Fisheries and Oceans Canada; University of New Brunswick","funders":"European Maritime and Fisheries Fund; Ministerie van Landbouw, Natuur en Voedselkwaliteit; Ocean Frontier Institute; Science Foundation Ireland","keywords":"Business; Corporate governance; Stakeholder; Experiential knowledge; Knowledge management; Quality (philosophy); Sociology of scientific knowledge; Environmental resource management; Public relations; Economics; Political science; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","open_science"],"consensus_categories":["sts"],"category_scores_codex":[0.01023666,0.00008010311,0.0001335657,0.0001403235,0.002216677,0.0001062737,0.00366083,0.00002639819,0.0005446139],"category_scores_gemma":[0.001665767,0.00004897118,0.00002113931,0.002807961,0.004969576,0.0005911147,0.008225131,0.0004371313,0.000001087961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001931154,"about_ca_system_score_gemma":0.0006355324,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01021492,"about_ca_topic_score_gemma":0.002520978,"domain_scores_codex":[0.9977292,0.0003483712,0.0002351456,0.0004300693,0.0009319233,0.0003252797],"domain_scores_gemma":[0.9984825,0.0002823115,0.0001154308,0.0009038746,0.0001235757,0.00009234143],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005140443,0.0001833762,0.6616743,0.000004723739,0.00001177981,0.000001143229,0.0236108,0.00006543029,0.0002400949,0.01812488,0.07608029,0.2199518],"study_design_scores_gemma":[0.0001614209,0.00004382548,0.8406227,0.00001327966,0.00001286146,2.883394e-7,0.05053521,0.005128665,0.00003912386,0.007765237,0.09558401,0.00009333307],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9424503,0.00003324607,0.0008686435,0.03168125,0.001040523,0.000771234,0.0001942667,0.00001589996,0.02294459],"genre_scores_gemma":[0.9971046,0.00005216567,0.001671372,0.0006231858,0.00004784011,0.00002581389,0.000006295852,0.00000256301,0.0004661672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2198585,"threshold_uncertainty_score":0.9997962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03619939121072555,"score_gpt":0.3323499466283577,"score_spread":0.2961505554176321,"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."}}