{"id":"W3134598146","doi":"10.54656/wysf8782","title":"Enlisting Students to Transcribe Historical Climate and Weather Data For Research: Building Knowledge Translation Via Classroom-Based Citizen Science","year":2021,"lang":"en","type":"article","venue":"Journal of Community Engagement and Scholarship","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Contextualization; Context (archaeology); Curriculum; Citizen science; Scientific literacy; Public engagement; Literacy; Community engagement; Sociology; Science education; Pedagogy; Political science; Library science; Public relations; Geography; Computer science; Interpretation (philosophy)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02398296,0.001134067,0.0006756969,0.002182476,0.007919941,0.009607293,0.003698306,0.002463858,0.005567166],"category_scores_gemma":[0.04492757,0.0008849414,0.0008142138,0.00128583,0.008662401,0.008447153,0.01467124,0.003985015,0.003493866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006239765,"about_ca_system_score_gemma":0.01411267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01223339,"about_ca_topic_score_gemma":0.02534541,"domain_scores_codex":[0.9815294,0.01244766,0.0005077975,0.002413466,0.00147347,0.001628204],"domain_scores_gemma":[0.9569569,0.02639614,0.001633078,0.005771402,0.005683944,0.003558594],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001209503,0.003006788,0.01644924,0.0005131561,0.00002230956,0.000972224,0.7062566,0.0005808633,0.006345367,0.006737411,0.008776556,0.2502186],"study_design_scores_gemma":[0.0002029121,0.001174144,0.009843417,0.00121737,0.00009490564,0.0005573182,0.6974346,0.005669211,0.01460552,0.02679987,0.2421788,0.0002219615],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8188844,0.0003687528,0.1220076,0.008343857,0.0002974743,0.003189669,0.0003629304,0.001303883,0.04524159],"genre_scores_gemma":[0.8296011,0.0005172772,0.1546993,0.00227366,0.00007244587,0.002575088,0.000476479,0.0003597133,0.009424907],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02398296,"threshold_uncertainty_score":0.1268355,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5895220652308965,"score_gpt":0.4604035621663398,"score_spread":0.1291185030645567,"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."}}