{"id":"W4288037704","doi":"10.1007/s13563-022-00333-3","title":"SQUIDs for magnetic and electromagnetic methods in mineral exploration","year":2022,"lang":"en","type":"article","venue":"Mineral Economics","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"Iron Ore Company (Canada)","funders":"Leibniz-Gemeinschaft; Universität zu Köln; Natural Sciences and Engineering Research Council of Canada; Westfälische Wilhelms-Universität Münster; Leibniz-Institut für Angewandte Geophysik","keywords":"Magnetometer; Gradiometer; Algorithm; Squid; Computer science; Physics; Nuclear magnetic resonance; Magnetic field; Quantum mechanics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003230162,0.0001249913,0.0002091336,0.0000979741,0.00012485,0.00004358685,0.0001356954,0.0000182314,0.0002969188],"category_scores_gemma":[0.000003825233,0.0001457369,0.00006227311,0.00009379116,0.00003729332,0.0001567767,0.0001220167,0.0001605081,0.000003192051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001159187,"about_ca_system_score_gemma":0.0001112186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001692065,"about_ca_topic_score_gemma":0.00001671944,"domain_scores_codex":[0.9990445,0.00008935664,0.0002459246,0.0002754065,0.00003814299,0.0003066161],"domain_scores_gemma":[0.9996334,0.0001003045,0.00004969161,0.0001407385,0.00001516256,0.0000607451],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000357914,0.0003851154,0.01408437,0.00003682579,0.00007101754,0.000001935899,0.002339177,0.006379954,0.06680267,0.2586411,0.004692204,0.6462077],"study_design_scores_gemma":[0.002691054,0.000381594,0.001093142,0.000002493145,0.00001903492,0.000002955944,0.00144594,0.8465379,0.001080654,0.1274792,0.01882673,0.000439303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9822174,0.0001024335,0.01381396,0.0004002546,0.00009430709,0.0003687725,0.00005159043,0.00001005608,0.002941251],"genre_scores_gemma":[0.9889011,0.00000858133,0.007859269,0.0000934584,0.0001816286,0.0005216323,0.0001185425,0.00002672625,0.00228904],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.840158,"threshold_uncertainty_score":0.5942977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02733170385492207,"score_gpt":0.3204114366956102,"score_spread":0.2930797328406881,"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."}}