{"id":"W4394545622","doi":"10.6084/m9.figshare.3846936","title":"Lab 2 - BIOL 2050 - Field training with plants.csv","year":2016,"lang":"en","type":"dataset","venue":"Figshare","topic":"Environmental Monitoring and Data Management","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Training (meteorology); Field (mathematics); Field training; Biology; Computer science; Geography; Engineering; Mathematics; Operations management; Meteorology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002047051,0.00231976,0.00131021,0.001593213,0.0007096694,0.001643979,0.00327049,0.001344662,0.1980926],"category_scores_gemma":[0.00486385,0.0008732841,0.00123367,0.002574702,0.0005028915,0.001974155,0.002347043,0.001759134,0.3276924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006815195,"about_ca_system_score_gemma":0.001340898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007071753,"about_ca_topic_score_gemma":0.01675303,"domain_scores_codex":[0.9991006,0.0001865576,0.00006292324,0.0003725612,0.0001429005,0.0001345765],"domain_scores_gemma":[0.9977138,0.0005171494,0.0000912587,0.001085208,0.0003132717,0.0002793368],"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":[0.0000721969,0.00002462383,0.0004944936,0.000211471,0.000011183,0.000008089151,0.0000237852,0.0001724543,0.0001366313,0.0002249668,0.9952406,0.003379667],"study_design_scores_gemma":[0.0004586544,0.00004872733,0.003047801,0.0001134646,0.00001787897,0.00003157989,0.00007206359,0.001514482,0.0008781948,0.002093864,0.9916933,0.00002992894],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004564046,0.00005060251,0.0009522714,0.0001417423,0.0001121424,0.00005952962,0.9781362,0.01613485,0.003956245],"genre_scores_gemma":[0.001559725,0.00004572279,0.002872271,0.0001856429,0.00002816693,0.0003448268,0.9893132,0.002954933,0.00269553],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8019074,"threshold_uncertainty_score":0.6626859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04031683883526116,"score_gpt":0.2197627525051338,"score_spread":0.1794459136698727,"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."}}