{"id":"W6931893799","doi":"10.5683/sp3/ehcfjc","title":"Data & code for Martin & Hargreaves Latitudinal gradients in seed germination time","year":2022,"lang":"en","type":"dataset","venue":"Borealis","topic":"Fractal and DNA sequence analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Code (set theory); Germination; Hydrology (agriculture); Global Positioning System","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":[0.002045577,0.003081834,0.001978579,0.005327718,0.001241597,0.003185574,0.003937589,0.001795395,0.2532611],"category_scores_gemma":[0.009968523,0.001593832,0.002063645,0.006524255,0.0007846304,0.002018731,0.002629142,0.002424189,0.2236205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001879218,"about_ca_system_score_gemma":0.00316846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02801799,"about_ca_topic_score_gemma":0.05540676,"domain_scores_codex":[0.998444,0.0001679923,0.0001743698,0.0004857308,0.0004129021,0.0003150566],"domain_scores_gemma":[0.9940913,0.001751448,0.0005710652,0.001761146,0.001159558,0.0006653704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00007322765,0.00002674507,0.0009911454,0.0005165808,0.00003426895,0.00001390602,0.00003581484,0.0002863481,0.0001879538,0.0004644888,0.9949545,0.00241499],"study_design_scores_gemma":[0.0003692894,0.00002141401,0.004057081,0.0002493545,0.00004568828,0.00003486733,0.00006063523,0.0003874954,0.0006408182,0.002155251,0.9919264,0.00005172866],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006071452,0.0000103916,0.00008016366,0.00001963038,0.0000120809,0.000008846046,0.9987173,0.0005790142,0.0005119347],"genre_scores_gemma":[0.0003628858,0.00002061185,0.0006238397,0.00004487163,0.000005933551,0.0001279321,0.9972379,0.0005513316,0.001024542],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2532611,"threshold_uncertainty_score":0.8472427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03872277772734577,"score_gpt":0.3136755318863977,"score_spread":0.274952754159052,"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."}}