{"id":"W2982058381","doi":"10.4095/211415","title":"Aeromagnetic residual total field survey, Rochester, New York","year":2000,"lang":"en","type":"report","venue":"","topic":"Image Processing and 3D Reconstruction","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Residual; Field (mathematics); Geology; Archaeology; Geography; Computer science; Mathematics; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003107429,0.0007663022,0.0004542804,0.001949382,0.0007594835,0.0003521214,0.0005063914,0.0002245715,0.01242449],"category_scores_gemma":[0.0003320347,0.0002308679,0.0001517005,0.001170167,0.0002269877,0.0004377169,0.000432058,0.0003529977,0.004277465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007707253,"about_ca_system_score_gemma":0.002021138,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1199378,"about_ca_topic_score_gemma":0.2125337,"domain_scores_codex":[0.9998553,0.0000136053,0.000003791894,0.00004600239,0.00006228725,0.0000188991],"domain_scores_gemma":[0.9996862,0.00002712637,0.00002385257,0.00003436991,0.0001788484,0.0000495364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.001003991,0.0004465406,0.07918885,0.0006216094,0.0001851053,0.001016597,0.0004167714,0.006689061,0.09092779,0.007492272,0.599035,0.2129764],"study_design_scores_gemma":[0.000119011,0.0002062061,0.4603939,0.00006920499,0.0001166997,0.0004527254,0.0002338494,0.00769744,0.01583176,0.0006774561,0.5141486,0.0000531942],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3552427,0.005566108,0.02485484,0.002407457,0.0008048946,0.0007914593,0.2387958,0.004137777,0.3673989],"genre_scores_gemma":[0.3569992,0.007633524,0.03668386,0.0002734776,0.0004946489,0.0005791635,0.1057146,0.0009807876,0.4906409],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1199378,"threshold_uncertainty_score":0.2384793,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05048801760776141,"score_gpt":0.2747456658262888,"score_spread":0.2242576482185274,"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."}}