{"id":"W4381085638","doi":"10.1002/lol2.10336","title":"Establishing a long‐term citizen science project? Lessons learned from the Community Lake Ice Collaboration spanning over 30 yr and 1000 lakes","year":2023,"lang":"en","type":"article","venue":"Limnology and Oceanography Letters","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; York University","keywords":"Citizen science; Term (time); Process (computing); Scale (ratio); Data collection; Political science; Public relations; Environmental resource management; Sociology; Geography; Environmental science; Computer science; Social science; Cartography","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":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.1604177,0.0006823163,0.0009091257,0.001608504,0.02016368,0.01476431,0.00496834,0.006778343,0.007337441],"category_scores_gemma":[0.09844022,0.0007230193,0.0009304947,0.001714078,0.01160475,0.01684187,0.0299603,0.009664537,0.001484996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009694467,"about_ca_system_score_gemma":0.07648788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01883911,"about_ca_topic_score_gemma":0.04984704,"domain_scores_codex":[0.8865315,0.08971661,0.002329414,0.00313658,0.005446459,0.01283948],"domain_scores_gemma":[0.7918256,0.08150402,0.006024109,0.009889445,0.03004966,0.08070731],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.000419439,0.00243583,0.03779052,0.002972621,0.000150344,0.004346508,0.4208962,0.001097056,0.00199899,0.03709277,0.1660893,0.3247105],"study_design_scores_gemma":[0.0001748682,0.0008632939,0.01021267,0.003957788,0.00003949089,0.0008722655,0.6275536,0.000953361,0.0008553293,0.02936119,0.3249829,0.0001732367],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.2457979,0.005827989,0.0303417,0.6728488,0.003444551,0.004469874,0.0007860777,0.000634623,0.03584842],"genre_scores_gemma":[0.8072299,0.004892278,0.08908379,0.07346891,0.0009486434,0.009653291,0.00112265,0.0005446071,0.01305601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9950317,"threshold_uncertainty_score":0.8483801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05021705140033598,"score_gpt":0.292646566390946,"score_spread":0.24242951499061,"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."}}