{"id":"W3192535644","doi":"10.1007/978-3-030-76694-8_13","title":"Ambiance Partition: An Interdisciplinary Reading, Measurement, and Notation of in Situ Experiences","year":2021,"lang":"en","type":"book-chapter","venue":"Springer tracts in civil engineering","topic":"Geographies of human-animal interactions","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Notation; Partition (number theory); Experiential learning; Reading (process); Representation (politics); Computer science; Mathematics education; Psychology; Linguistics; Mathematics; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005564569,0.0002044619,0.0003130467,0.0004845446,0.00009930282,0.0000780233,0.0001809152,0.0001839978,0.0001954619],"category_scores_gemma":[0.0001361269,0.000263131,0.00006945087,0.0001326349,0.0001535075,0.0005867637,0.00009017975,0.0004172165,0.000002518252],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001852171,"about_ca_system_score_gemma":0.00006077587,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002795765,"about_ca_topic_score_gemma":0.145779,"domain_scores_codex":[0.9984652,0.00002596163,0.0004447323,0.0003696919,0.0004340427,0.0002603889],"domain_scores_gemma":[0.9993663,0.00007453287,0.000164867,0.0001877277,0.0001207062,0.00008590507],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001538013,0.0007935053,0.006993333,0.001570444,0.0003148758,0.0005981993,0.6261005,0.01225725,0.03197979,0.3113438,0.0003100203,0.007584488],"study_design_scores_gemma":[0.003728325,0.001532345,0.4075003,0.0488404,0.0004309,0.00006916738,0.4038554,0.003130737,0.01142385,0.02406206,0.08668111,0.008745405],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5853248,0.002567311,0.00008860284,0.0002762597,0.001468592,0.0005184177,0.000006471223,0.0001406769,0.4096089],"genre_scores_gemma":[0.9972274,0.0003420613,0.0003516664,0.000007561513,0.0001400527,0.00004422304,0.000005213172,0.00002838451,0.001853445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4119026,"threshold_uncertainty_score":0.9999821,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03971481950670006,"score_gpt":0.3087651076596171,"score_spread":0.269050288152917,"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."}}