{"id":"W4281553608","doi":"10.1111/ssm.12528","title":"Citizen science in K–12 school‐based learning settings","year":2022,"lang":"en","type":"article","venue":"School Science and Mathematics","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Citizen science; Crowdsourcing; Data collection; Scope (computer science); Citizen journalism; Categorization; Science learning; Science education; Sociology; Mathematics education; Public relations; Political science; Pedagogy; Psychology; Computer science; Social science","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00334487,0.0001201756,0.0001296244,0.0001819256,0.001497415,0.0002280644,0.0007159321,0.00001940539,0.03150565],"category_scores_gemma":[0.001225822,0.0001138411,0.00002315872,0.002207453,0.00138258,0.0005528845,0.0009370852,0.0003184266,0.0003998097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001463375,"about_ca_system_score_gemma":0.0002363312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005868111,"about_ca_topic_score_gemma":0.0000288795,"domain_scores_codex":[0.9972754,0.00002675453,0.0002251622,0.0004509058,0.001457723,0.0005640242],"domain_scores_gemma":[0.9992429,0.00004088771,0.0001045846,0.0002454503,0.0000308832,0.000335279],"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":[0.00004360692,0.0009801388,0.4532607,0.000157688,0.00000473093,0.00006984509,0.006099289,0.003336433,0.4818848,0.01573954,0.03094989,0.007473342],"study_design_scores_gemma":[0.003052208,0.000693207,0.6852425,0.0001284989,0.0000351628,0.0001787919,0.09172152,0.05853954,0.01326098,0.01062145,0.1345463,0.001979811],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9358158,0.00001493458,0.00006207219,0.0008581397,0.0001033519,0.0002115889,0.000007517124,0.00005763796,0.06286892],"genre_scores_gemma":[0.9967741,0.00001060743,0.001439549,0.0009646325,0.000009517382,0.00005185988,0.000002826283,0.000007956949,0.0007389751],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4686238,"threshold_uncertainty_score":0.9998025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01859223964744771,"score_gpt":0.2531887213584543,"score_spread":0.2345964817110066,"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."}}