{"id":"W2945905657","doi":"10.1002/ecs2.2753","title":"Enhancing collaboration between ecologists and computer scientists: lessons learned and recommendations forward","year":2019,"lang":"en","type":"article","venue":"Ecosphere","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Global Lake Ecological Observatory Network; National Science Foundation","keywords":"Computer science; Software; Ecology; Bridging (networking); Data science; Big data; Supercomputer; Biology; Data mining","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000171777,0.00007834392,0.0001025092,0.000008391039,0.0001813833,0.0001149661,0.00006467071,0.00005199917,0.04605071],"category_scores_gemma":[0.00001676922,0.00007696995,0.00001367313,0.0001640636,0.0000838218,0.0002290255,0.0001551782,0.00006053344,0.001988623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001924119,"about_ca_system_score_gemma":0.000007033675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004234246,"about_ca_topic_score_gemma":0.002295651,"domain_scores_codex":[0.9993363,0.00002993426,0.0001191908,0.0002577227,0.00009453074,0.0001623216],"domain_scores_gemma":[0.9996928,0.00005195659,0.00005740073,0.0001106143,0.00001069601,0.00007653539],"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":[0.00001915244,0.0001299495,0.4265689,0.00006577528,0.00004173382,0.000003751502,0.00152662,0.00005655444,0.006202074,0.007433622,0.4652442,0.09270769],"study_design_scores_gemma":[0.0005104892,0.00009829958,0.7750919,0.00001449731,0.0000119278,0.000002976197,0.001827757,0.0004941325,0.0008676896,0.0003783498,0.220509,0.0001929685],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9636419,0.00003815467,0.000458095,0.004489929,0.0001826211,0.0001809557,0.00007700689,0.0000355829,0.03089573],"genre_scores_gemma":[0.9965096,0.0001021506,0.0008167742,0.0002096399,0.00002691426,0.000008340435,0.00009832798,0.000006397919,0.002221854],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.348523,"threshold_uncertainty_score":0.9987884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02783461812951182,"score_gpt":0.2937586407481101,"score_spread":0.2659240226185983,"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."}}