{"id":"W2100265584","doi":"10.1111/j.1467-9922.2007.00407.x","title":"Word Family Size and French‐Speaking Children's Segmentation of Existing Compounds","year":2007,"lang":"en","type":"article","venue":"Language Learning","topic":"Language Development and Disorders","field":"Psychology","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Psychology; Linguistics; Meaning (existential); Analogy; Segmentation; Text segmentation; Word (group theory); Developmental psychology; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0008397003,0.0006008739,0.0002845163,0.0009002738,0.0003813188,0.0011947,0.0002235645,0.000547219,0.005565197],"category_scores_gemma":[0.00401168,0.0003210106,0.0003650899,0.0002795937,0.0007955752,0.0006188217,0.0004199909,0.0003720641,0.0004776171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000573243,"about_ca_system_score_gemma":0.0003905153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0127927,"about_ca_topic_score_gemma":0.01755999,"domain_scores_codex":[0.9995099,0.0001027016,0.00004781301,0.0001120254,0.0001261393,0.0001013253],"domain_scores_gemma":[0.9957246,0.002233017,0.001312144,0.0001299908,0.0002895548,0.0003107734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001062156,0.0003896465,0.8337851,0.0002289527,0.0001207632,0.004030475,0.03497262,0.0006889539,0.09407377,0.0006501058,0.0004954539,0.02950204],"study_design_scores_gemma":[0.00001965197,0.0004409176,0.9852505,0.00003184771,0.00006166279,0.001786677,0.005926381,0.0003839034,0.004990597,0.0001984949,0.0008803988,0.00002902174],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9991875,0.00006063181,0.00002944865,0.00001369935,0.000001201336,0.000001353093,0.00002402241,0.00000597685,0.0006760891],"genre_scores_gemma":[0.9992185,0.0001017689,0.0001474438,0.00001209004,0.000001256357,0.000004314723,0.00006406045,0.000005509139,0.0004450185],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0127927,"threshold_uncertainty_score":0.02543646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602768943730018,"score_gpt":0.3093217535163222,"score_spread":0.293294064079022,"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."}}