{"id":"W2071421734","doi":"10.1080/03632415.2013.838133","title":"Smartphones and Digital Tablets: Emerging Tools for Fisheries Professionals","year":2013,"lang":"en","type":"article","venue":"Fisheries","topic":"Mobile and Web Applications","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Trent University; Carleton University","funders":"","keywords":"Fishery; Business; Data science; Computer science; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.00005717582,0.0001115406,0.000129364,0.00002821639,0.0002580712,0.001189249,0.0003015384,0.00004406199,0.0001102873],"category_scores_gemma":[0.00008773351,0.00009318178,0.00003390124,0.0001287342,0.00007202507,0.003317064,0.0001974842,0.00004624534,0.00003937673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007241349,"about_ca_system_score_gemma":0.00003473414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002366477,"about_ca_topic_score_gemma":0.000006958493,"domain_scores_codex":[0.9992327,0.000009306074,0.0001705264,0.0002646562,0.0001052571,0.0002175571],"domain_scores_gemma":[0.9992656,0.0002151265,0.00005564829,0.0003044483,0.00009401777,0.00006515752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000007769,0.0001268651,0.01870132,0.0001368889,0.0000427987,0.000001244063,0.003131384,0.000001912216,0.001347608,0.04941586,0.4490347,0.4780517],"study_design_scores_gemma":[0.000274607,0.00008047377,0.02841443,0.00003718818,0.000005062639,0.000009033736,0.0008074412,0.001819401,0.001603419,0.03208189,0.9345212,0.0003458883],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5260473,0.0005839835,0.3828554,0.06759746,0.0009704413,0.003055007,0.000121317,0.0008346589,0.01793436],"genre_scores_gemma":[0.9422922,0.00004261855,0.04192435,0.001611441,0.0002149884,0.003651955,0.00006274552,0.00002690727,0.01017281],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4854865,"threshold_uncertainty_score":0.9998476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01917338584804978,"score_gpt":0.2385562823212364,"score_spread":0.2193828964731866,"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."}}