{"id":"W4286634484","doi":"10.1101/2022.07.02.498308","title":"Rapid monitoring for ecological persistence","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Liber Ero Foundation; Royal Commission for the Exhibition of 1851; National Science Foundation","keywords":"Persistence (discontinuity); Convention on Biological Diversity; Ecology; Ecological network; Environmental resource management; Probabilistic logic; Ecosystem; Biodiversity; Computer science; Environmental science; Biology; Artificial intelligence; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002319605,0.0002780108,0.0003988162,0.001980928,0.0003875051,0.001143272,0.0005736661,0.0007234132,0.001386393],"category_scores_gemma":[0.008658295,0.0002279553,0.0002197113,0.001309689,0.0005717246,0.001770041,0.001220015,0.001142506,0.0003687397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000555287,"about_ca_system_score_gemma":0.0002925475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00123742,"about_ca_topic_score_gemma":0.001333998,"domain_scores_codex":[0.9989713,0.0002932622,0.00004696698,0.0003613001,0.0002479713,0.00007929357],"domain_scores_gemma":[0.9902653,0.004027746,0.003056205,0.001282272,0.0009304148,0.0004380511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004743419,0.0001870557,0.5919074,0.0005994682,0.0004749706,0.0002637996,0.001017315,0.04292185,0.08426175,0.01648008,0.008025127,0.2533868],"study_design_scores_gemma":[0.00004099668,0.0004060014,0.5703508,0.0001974945,0.0002310866,0.0008828084,0.0008199452,0.3093288,0.04563179,0.05494093,0.01701706,0.0001521717],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8271762,0.001621329,0.1604103,0.0008100788,0.00007559011,0.00007370117,0.002566679,0.001133833,0.006132409],"genre_scores_gemma":[0.9822128,0.0002206388,0.01578451,0.00005306072,0.00004720633,0.00005055959,0.000943246,0.00004241176,0.0006455194],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002319605,"threshold_uncertainty_score":0.01226741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07086661196978569,"score_gpt":0.2197895392342421,"score_spread":0.1489229272644564,"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."}}