{"id":"W4393711349","doi":"10.5281/zenodo.4694950","title":"Directly-Georeferenced Hyperspectral Point Cloud (DHPC) from the Mer Bleue Peatland (example dataset)","year":2021,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; McGill University","funders":"","keywords":"Georeference; Hyperspectral imaging; Peat; Point cloud; Cloud computing; Remote sensing; Point (geometry); Environmental science; Geology; Geography; Computer science; Physical geography; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004543209,0.001497023,0.000851609,0.00201583,0.001063715,0.001337314,0.001914598,0.000940463,0.02068372],"category_scores_gemma":[0.001353509,0.0005746243,0.001147606,0.003060421,0.0004265723,0.0006655628,0.001392736,0.001039164,0.02083137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002828773,"about_ca_system_score_gemma":0.004112007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4795074,"about_ca_topic_score_gemma":0.754366,"domain_scores_codex":[0.9992741,0.0000353618,0.00003161777,0.0001880269,0.0003165783,0.0001543837],"domain_scores_gemma":[0.9992052,0.0000519744,0.0000300101,0.0001925658,0.0004423423,0.00007795119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002584727,0.00009630448,0.007848023,0.0009436566,0.0001883291,0.0002413229,0.0001393188,0.002799652,0.005378011,0.0006019946,0.9563646,0.02514029],"study_design_scores_gemma":[0.000340622,0.00004055315,0.06358416,0.0003516935,0.00009054989,0.0003364892,0.0004328067,0.006421684,0.006882671,0.0009399836,0.9204642,0.0001146727],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006803997,0.0003209469,0.001253646,0.0001052209,0.00009419287,0.00008833298,0.9842921,0.003268184,0.003773411],"genre_scores_gemma":[0.005106115,0.00008842767,0.002794127,0.00002233962,0.000007544187,0.00006494734,0.9902515,0.0003045329,0.001360442],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4795074,"threshold_uncertainty_score":0.9534327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03381871681111034,"score_gpt":0.2399248877000666,"score_spread":0.2061061708889562,"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."}}