{"id":"W4291271052","doi":"10.1002/nsg.12231","title":"Detecting subsea permafrost layers on marine seismic data: An appraisal from forward modelling","year":2022,"lang":"en","type":"article","venue":"Near Surface Geophysics","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Geological Survey of Canada","funders":"","keywords":"Permafrost; Geology; Subsea; Reflection (computer programming); Seafloor spreading; Stratigraphy; Geomorphology; Geotechnical engineering; Remote sensing; Seismology; Geophysics; Tectonics; Oceanography","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":[0.001763138,0.001086994,0.00065458,0.001367427,0.0002323279,0.00102134,0.0007415367,0.0008303908,0.0009495658],"category_scores_gemma":[0.00367312,0.0004077873,0.0007703578,0.0007700048,0.0003099931,0.0008290219,0.0004924149,0.000468669,0.0004159408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005736451,"about_ca_system_score_gemma":0.0009205535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01835212,"about_ca_topic_score_gemma":0.01272606,"domain_scores_codex":[0.999546,0.0001522703,0.00003225033,0.00005478438,0.0001918538,0.00002282627],"domain_scores_gemma":[0.9972389,0.002036224,0.0001121536,0.0001025771,0.0004586149,0.00005160243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003581064,0.0001004303,0.02646936,0.0005308525,0.0002071263,0.0002719607,0.0001383458,0.6178082,0.01158481,0.001699972,0.0008735341,0.3399572],"study_design_scores_gemma":[0.000005436784,0.00005891662,0.004811368,0.00005326811,0.0000318218,0.00005017581,0.00003609843,0.9919636,0.001580835,0.0005741944,0.0008174043,0.00001683416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5038503,0.009879891,0.4739893,0.002298761,0.0002354905,0.0001986058,0.001108095,0.001462609,0.006976913],"genre_scores_gemma":[0.8450738,0.006723374,0.1454193,0.00007762571,0.00009048168,0.00009485438,0.0007315968,0.000139353,0.001649662],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01835212,"threshold_uncertainty_score":0.03649062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03556530437393982,"score_gpt":0.2482739985328482,"score_spread":0.2127086941589083,"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."}}