{"id":"W6961202120","doi":"10.14288/1.0396722","title":"Examining the relationship between Landsat-derived spectral reflectance and multispectral Light Detection and Ranging (LiDAR)-derived intensity in Petawawa Research Forest, Ontario, Canada","year":2021,"lang":"en","type":"dataset","venue":"Open Collections","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multispectral image; Ranging; Multispectral Scanner; Reflectivity; Multispectral pattern recognition; Irradiance; Spectral bands; Range (aeronautics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006385616,0.0005389923,0.0003492378,0.001043722,0.001651899,0.001214157,0.0009528206,0.000314491,0.001209519],"category_scores_gemma":[0.00199051,0.0003402942,0.0003646876,0.002603765,0.0006430246,0.0004304656,0.0003848972,0.00032894,0.0002553974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01943194,"about_ca_system_score_gemma":0.01600108,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9959765,"about_ca_topic_score_gemma":0.9983083,"domain_scores_codex":[0.9992568,0.00003818788,0.00003201571,0.0002105319,0.000291955,0.0001704931],"domain_scores_gemma":[0.998071,0.0001900349,0.0001867649,0.00005873738,0.001304952,0.0001884496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001697554,0.00004728984,0.9746382,0.00009393718,0.000129675,0.0002193193,0.0012096,0.001822485,0.005709284,0.0002030201,0.001275477,0.01448198],"study_design_scores_gemma":[0.00000396528,0.000008950582,0.9952722,0.00001391341,0.00002776928,0.0000236038,0.000942732,0.002137632,0.0003496972,0.0000172159,0.001190751,0.00001150958],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9922491,0.0005769045,0.0009073757,0.0001305184,0.00001057287,0.00004454572,0.002729555,0.00003981136,0.003311593],"genre_scores_gemma":[0.9946464,0.0002038397,0.0009046443,0.00003261218,0.000002623892,0.00001657875,0.001840112,0.00001584188,0.002337263],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.01943194,"threshold_uncertainty_score":0.1409893,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1073140964667804,"score_gpt":0.3278836947739577,"score_spread":0.2205695983071772,"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."}}