{"id":"W4385370932","doi":"10.1016/j.respol.2023.104864","title":"Explaining technical change and its impacts over the very long term: The case of the Atlantic sardine fishery in France from 1900 to 2017","year":2023,"lang":"en","type":"article","venue":"Research Policy","topic":"Economic and Technological Innovation","field":"Economics, Econometrics and Finance","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Rimouski","funders":"CHIST-ERA; Agence Nationale de la Recherche; France Énergies Marines","keywords":"Sardine; Creatures; Haddock; Fishery; Technological change; Stock (firearms); Technical change; Fisheries science; Economy; Geography; Fishing; Economic geography; Fish <Actinopterygii>; Fisheries management; Economics; Productivity","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"medium","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001782832,0.00008381793,0.0001986851,0.0003662813,0.0001966323,0.00006542855,0.0004414693,0.0001096122,0.00004119423],"category_scores_gemma":[0.00155918,0.00005064436,0.00004325988,0.001416628,0.0001561897,0.0001699686,0.0005150032,0.0004368492,0.0001047411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009424289,"about_ca_system_score_gemma":0.00002726363,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007241192,"about_ca_topic_score_gemma":0.0008427002,"domain_scores_codex":[0.9989364,0.00005047644,0.0003264257,0.0002479187,0.00004822675,0.0003905117],"domain_scores_gemma":[0.9988707,0.000496193,0.0001033705,0.0004575199,0.00002735607,0.00004488291],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000016706,0.00003979703,0.816015,0.00003769099,0.0000300443,0.00009365609,0.0008473293,0.00001348168,0.0003417883,0.1714758,0.004952346,0.00613634],"study_design_scores_gemma":[0.0001918353,0.00003236961,0.9843613,0.00005042657,8.085408e-7,0.00001646131,0.00009869089,0.0007255953,0.00008734865,0.01216537,0.002200594,0.00006920741],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.980087,0.0005926581,0.000003879084,0.01780516,0.00004697906,0.0004468393,0.00008495033,0.00002210791,0.0009104043],"genre_scores_gemma":[0.9985243,0.0004873756,0.00000465805,0.000561613,0.000198114,0.00008589077,0.000003716376,0.00001084768,0.0001234677],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1683463,"threshold_uncertainty_score":0.9993697,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3040450649044375,"score_gpt":0.3966165003426498,"score_spread":0.09257143543821228,"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."}}