{"id":"W4410502950","doi":"10.1002/ecy.70074","title":"<scp>TropiRoot</scp> 1.0: Database of tropical root characteristics across environments","year":2025,"lang":"en","type":"article","venue":"Ecology","topic":"Plant nutrient uptake and metabolism","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Database; Biome; Tropics; Ecology; Geography; Root (linguistics); Biodiversity; Biology; Ecosystem; Computer science","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":[0.001817434,0.001232106,0.001588512,0.01402629,0.0006711937,0.00367438,0.002364269,0.001308289,0.1292219],"category_scores_gemma":[0.01874953,0.0006635816,0.0009847493,0.02208882,0.0006743756,0.003635474,0.004527123,0.001442022,0.05069176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001253412,"about_ca_system_score_gemma":0.004436801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01340653,"about_ca_topic_score_gemma":0.01907965,"domain_scores_codex":[0.99858,0.0001533891,0.0005102297,0.0002497303,0.0003561033,0.0001506205],"domain_scores_gemma":[0.9858813,0.005124119,0.002674144,0.001598089,0.003466883,0.001255403],"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":[0.0003194564,0.00003109794,0.004432686,0.01319008,0.0001222671,0.0003009711,0.0005032473,0.000523607,0.0028819,0.004662607,0.9381232,0.03490874],"study_design_scores_gemma":[0.0001408689,0.00003099572,0.01657783,0.002137778,0.00009159631,0.0002376294,0.0002091827,0.0004524107,0.001297425,0.002231854,0.97651,0.00008248367],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007016421,0.0002790774,0.0007384134,0.0001633317,0.00003108949,0.00007091435,0.9931376,0.001570048,0.003308016],"genre_scores_gemma":[0.004582347,0.0006356185,0.004302736,0.0002529066,0.00005401953,0.0004811578,0.9865279,0.001461161,0.001702212],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1292219,"threshold_uncertainty_score":0.4322902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01346803325093995,"score_gpt":0.2317939975425256,"score_spread":0.2183259642915856,"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."}}