{"id":"W4283753693","doi":"10.3389/frsen.2022.869291","title":"Discovering Inclusivity in Remote Sensing: Leaving No One Behind","year":2022,"lang":"en","type":"article","venue":"Frontiers in Remote Sensing","topic":"Conferences and Exhibitions Management","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McMaster University; Laurentian University","funders":"","keywords":"Diversity (politics); Field (mathematics); Residence; Face (sociological concept); Publishing; Public relations; Action plan; Audit; Limiting; Representation (politics); Institution; Inclusion (mineral); Political science; Plan (archaeology); Action (physics); Sociology; Law; Social science; Geography; Engineering; Management; Politics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2069898,0.0006429449,0.001190787,0.008821885,0.01432705,0.03369521,0.002845466,0.002940825,0.003843208],"category_scores_gemma":[0.4690337,0.001015072,0.0006389176,0.009312665,0.01954245,0.02111726,0.0162769,0.0066047,0.001200493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005687743,"about_ca_system_score_gemma":0.01795508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002034598,"about_ca_topic_score_gemma":0.003593259,"domain_scores_codex":[0.705985,0.1411,0.02971675,0.0171781,0.09991514,0.006104964],"domain_scores_gemma":[0.3272426,0.3435502,0.1485558,0.05702854,0.1029648,0.02065819],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005771174,0.0001535162,0.168583,0.003756225,0.0004356728,0.002315163,0.3573141,0.0003984697,0.007104842,0.06528424,0.04242023,0.3516575],"study_design_scores_gemma":[0.00007458244,0.0004329794,0.07930993,0.006277366,0.0002660862,0.003834941,0.2461446,0.001341774,0.006048703,0.09861271,0.5572314,0.0004248143],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5989561,0.02262635,0.04119581,0.2192967,0.01206585,0.0007138085,0.0004468205,0.0008138296,0.1038847],"genre_scores_gemma":[0.9497306,0.004809442,0.01484934,0.01641192,0.004210854,0.0002657191,0.0001635289,0.0004805049,0.009078109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2069898,"threshold_uncertainty_score":0.9779227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02313936478825317,"score_gpt":0.2722033321069096,"score_spread":0.2490639673186564,"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."}}