{"id":"W4205438450","doi":"10.1139/facets-2021-0013","title":"Having it all: hybridizing conventional and community science monitoring for enhanced data quality and cost savings","year":2021,"lang":"en","type":"article","venue":"FACETS","topic":"Fire effects on ecosystems","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service; University of New Brunswick","funders":"","keywords":"Citizen science; Wildlife; Population; Data quality; Environmental resource management; Geography; Environmental science; Environmental planning; Ecology; Engineering; Operations management; Sociology; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002842884,0.0001074331,0.0001564393,0.00001820507,0.0007311309,0.0001677415,0.0004006511,0.00003172184,0.00004456665],"category_scores_gemma":[0.001072138,0.0001161715,0.0000154864,0.0001269234,0.0003162843,0.0007221745,0.001322567,0.0001901768,0.00001494747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001321033,"about_ca_system_score_gemma":0.00002767543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009982541,"about_ca_topic_score_gemma":0.0004051798,"domain_scores_codex":[0.9985915,0.0001968199,0.0002026418,0.0004192817,0.0003073644,0.0002824263],"domain_scores_gemma":[0.9987126,0.0004973208,0.00009266793,0.000545273,0.00002388923,0.0001282502],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000008666202,0.00006321062,0.07838475,0.00009918309,0.00001017915,0.000002156505,0.0007653148,0.00001163972,0.9098014,0.0000310271,0.0003600038,0.01046242],"study_design_scores_gemma":[0.0008948115,0.00005454613,0.7504994,0.0002042586,0.00002046657,0.00003637369,0.00119927,0.006930798,0.2323939,0.0001508077,0.007263731,0.000351704],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968635,0.00009096562,0.001603953,0.0003236339,0.0002572481,0.0003056214,0.00007653249,0.00002894513,0.0004495826],"genre_scores_gemma":[0.9965218,0.00001589074,0.003173177,0.00008863092,0.00002739722,0.00002177852,0.00003428636,0.00000939101,0.0001075901],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6774076,"threshold_uncertainty_score":0.5623342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1242351517797534,"score_gpt":0.3736295436690661,"score_spread":0.2493943918893127,"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."}}