{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005786747,0.0001396617,0.0001284427,0.0001069408,0.001473477,0.0002988957,0.0003300451,0.00008140225,0.0006680432],"category_scores_gemma":[0.00008949912,0.0001120414,0.0000323241,0.001391116,0.001976262,0.0004776628,0.0003794845,0.0003315594,0.00003385953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004138122,"about_ca_system_score_gemma":0.00001215939,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002915236,"about_ca_topic_score_gemma":0.003451276,"domain_scores_codex":[0.9987783,0.0001891891,0.0001350999,0.0003131004,0.0002131234,0.0003711501],"domain_scores_gemma":[0.999281,0.0002908662,0.00007989368,0.0002713368,0.00001335671,0.00006348803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002781671,0.00002667756,0.9642128,0.000008151406,0.000020922,0.0000110966,0.007062756,0.000006512957,0.01784322,0.0003968657,0.008737182,0.001645982],"study_design_scores_gemma":[0.0003346456,0.00003242971,0.9872584,0.00001654717,0.0000210841,0.000004199683,0.007753534,0.00004668765,0.0003137627,0.0001248877,0.003951513,0.0001423182],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824768,0.00008137937,0.00001978393,0.01584763,0.0001381824,0.0001697224,0.0001520343,0.0001201311,0.0009943306],"genre_scores_gemma":[0.9957123,0.0003546569,0.00001790955,0.00370595,0.00002953675,0.00001395778,0.0001319708,0.000008052069,0.00002563188],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02304558,"threshold_uncertainty_score":0.9998265,"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."}}