{"id":"W4400747309","doi":"10.1080/00368121.2024.2376753","title":"Discovering DIY oceanography: building floats to track deep ocean currents","year":2024,"lang":"en","type":"article","venue":"Science Activities","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tula Foundation; University of British Columbia","funders":"","keywords":"Track (disk drive); Oceanography; Ocean science; Science education; Marine biology; Indian ocean; Marine engineering; Meteorology; Environmental science; Mathematics education; Geology; Engineering; Geography; Mechanical engineering; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0002522013,0.0001314629,0.0001121336,0.0003400507,0.0002151565,0.0006777399,0.0005213743,0.00002274215,0.0000101374],"category_scores_gemma":[0.000003048866,0.0001196627,0.00005576145,0.0008866795,0.0001293426,0.001290092,0.0001252121,0.0001343201,0.0000282909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009817382,"about_ca_system_score_gemma":0.00002883979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001220019,"about_ca_topic_score_gemma":0.0000100621,"domain_scores_codex":[0.9989393,0.00001273941,0.0001484085,0.0002335075,0.0003174297,0.0003486587],"domain_scores_gemma":[0.9995258,0.00003885454,0.00001019288,0.0002986325,0.00001582747,0.0001106701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000009228049,0.00005694882,0.006229402,0.0005978454,0.0000909837,0.00001384019,0.03730757,0.04357509,0.7343556,0.003433255,0.0005117618,0.1738185],"study_design_scores_gemma":[0.0002054207,0.0001065066,0.00390415,0.001039154,0.00002730145,0.00003891036,0.01192822,0.1534704,0.7670467,0.001769276,0.05930869,0.001155313],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9779609,0.0007159469,0.01866637,0.00006824572,0.0005886554,0.0001062254,0.000006302576,0.0005201197,0.001367195],"genre_scores_gemma":[0.9989973,0.0000426404,0.0007583897,0.00001818512,0.00008426312,0.000009561231,8.79573e-7,0.00002143875,0.00006735967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1726632,"threshold_uncertainty_score":0.6535462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01346467974402188,"score_gpt":0.2606834308323283,"score_spread":0.2472187510883064,"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."}}