{"id":"W2736690020","doi":"10.1093/biosci/bix098","title":"Envisioning the Future of Aquatic Animal Tracking: Technology, Science, and Application","year":2017,"lang":"en","type":"article","venue":"BioScience","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":165,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University; Dalhousie University; University of British Columbia; University of Windsor; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Cyberinfrastructure; Operationalization; Key (lock); Data science; Computer science; Tracking (education); Citizen science; Environmental resource management; Knowledge management; Biology; Environmental science","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":["sts"],"consensus_categories":["sts"],"category_scores_codex":[0.000720005,0.00005131809,0.00005705598,0.00004221632,0.002020722,0.00005040766,0.0008354749,0.00002283488,0.00001546773],"category_scores_gemma":[0.0002489726,0.00003295642,0.00000774058,0.0002925247,0.006369514,0.0003397414,0.0007713906,0.00005560594,0.00002029035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002348533,"about_ca_system_score_gemma":0.00001024484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002966697,"about_ca_topic_score_gemma":0.0001994996,"domain_scores_codex":[0.9992881,0.000006122271,0.00007779034,0.0002449812,0.0002258474,0.000157142],"domain_scores_gemma":[0.9994658,0.00002027878,0.000123666,0.0003601394,0.000009543182,0.00002063733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000003680669,0.00003148347,0.8720586,0.000004228224,0.000001952743,6.254883e-7,0.0002184519,0.000003956918,0.06428506,0.01419712,0.001296028,0.04789886],"study_design_scores_gemma":[0.00005370368,0.00004311678,0.9888429,0.000003406952,0.000004266571,0.000001647031,0.0003199799,0.0009546642,0.002812219,0.001393341,0.005523885,0.00004684374],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9771501,0.0000503479,0.0003199999,0.01077129,0.0001293363,0.000202481,5.058129e-7,0.00001892233,0.01135706],"genre_scores_gemma":[0.9991617,0.00004340059,0.0005400496,0.0001591473,0.00001262481,0.00001174045,3.999922e-8,0.000001368601,0.00006999398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1167844,"threshold_uncertainty_score":0.9992785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007464244712416907,"score_gpt":0.2432000020720496,"score_spread":0.2357357573596327,"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."}}