{"id":"W2551446035","doi":"","title":"Using large area imaging to integrate biogeochemical data across spatial scales","year":2014,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Biogeochemical cycle; Remote sensing; Environmental science; Geology","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":[0.0003911792,0.0004559591,0.0003266711,0.001462815,0.0002537158,0.000995672,0.0004755144,0.0005462692,0.001341894],"category_scores_gemma":[0.001309245,0.000411681,0.0004200299,0.001787357,0.0002117929,0.00148172,0.0007284845,0.0005554643,0.0003937116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000307074,"about_ca_system_score_gemma":0.0003968038,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007639751,"about_ca_topic_score_gemma":0.01628223,"domain_scores_codex":[0.999845,0.000022373,0.000008655531,0.00005590251,0.00004630143,0.00002173265],"domain_scores_gemma":[0.999541,0.0001540755,0.00006696509,0.00009234796,0.0001085388,0.00003703676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002016279,0.000438224,0.04676539,0.000234627,0.0005139487,0.0003898254,0.0003168675,0.1048668,0.3509229,0.003473358,0.00788063,0.4839957],"study_design_scores_gemma":[0.00004201096,0.00006146663,0.05300701,0.00002687217,0.0001462376,0.0002558554,0.0002268089,0.8877123,0.03872281,0.01014588,0.009577805,0.00007491128],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3122555,0.001121401,0.6675776,0.0008402315,0.0002617755,0.0001150096,0.002519859,0.005237358,0.01007128],"genre_scores_gemma":[0.551806,0.0006041242,0.4429975,0.0002817109,0.0001498909,0.00008672701,0.002192349,0.0004099104,0.001471744],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007639751,"threshold_uncertainty_score":0.0151906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0468255685105318,"score_gpt":0.299349860235181,"score_spread":0.2525242917246492,"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."}}