{"id":"W4394148681","doi":"10.6084/m9.figshare.3850758","title":"Lab 2, Field Dataset 1: Utilizing Quadrats to Examine the Abundance and Diversity of Plant Species Within the Danby Woods Grasslands.","year":2016,"lang":"en","type":"dataset","venue":"Figshare","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quadrat; Abundance (ecology); Plant diversity; Diversity (politics); Geography; Field (mathematics); Ecology; Species diversity; Grassland; Plant species; Forestry; Biology; Mathematics; Shrub; Sociology; Anthropology","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":[0.001278528,0.001680002,0.0009706357,0.002318953,0.0006594207,0.001313465,0.002129184,0.001253853,0.01918566],"category_scores_gemma":[0.004124356,0.0005484698,0.00124147,0.003286647,0.0003759578,0.001271374,0.001142345,0.001022978,0.02154607],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00113732,"about_ca_system_score_gemma":0.001219724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0545955,"about_ca_topic_score_gemma":0.1452749,"domain_scores_codex":[0.9990483,0.0001629158,0.00008749543,0.0003831442,0.0001822257,0.000135914],"domain_scores_gemma":[0.9982161,0.0004094504,0.0001960131,0.0004485057,0.000559217,0.0001707366],"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":[0.000383672,0.0003385238,0.05160329,0.002022756,0.0003628591,0.0001644822,0.0003043477,0.002807169,0.002228554,0.0005540809,0.9215209,0.01770933],"study_design_scores_gemma":[0.0009697618,0.0002520607,0.2873947,0.0007874054,0.0002664056,0.0002871649,0.002350256,0.008565606,0.003097513,0.00209369,0.6937503,0.0001850957],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005369251,0.0001151472,0.0003863312,0.00006409385,0.00003118601,0.0000275023,0.9923672,0.0008030813,0.0008361299],"genre_scores_gemma":[0.005872113,0.00003318141,0.001716142,0.00004450613,0.00000881675,0.0001046696,0.9916151,0.0000920462,0.0005134571],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0545955,"threshold_uncertainty_score":0.1085554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03806483785897408,"score_gpt":0.2363443374421677,"score_spread":0.1982794995831937,"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."}}