{"id":"W3192704549","doi":"10.1177/25148486211036113","title":"Milking economies: Multispecies entanglements in the infant formula industry","year":2021,"lang":"en","type":"article","venue":"Environment and Planning E Nature and Space","topic":"Geographies of human-animal interactions","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Commission; Infant formula; China; Breed; Factory (object-oriented programming); Dairy industry; Race (biology); Empire; Geography; Economy; Sociology; Political science; Ecology; Law; Economics; Biology; Gender studies; Archaeology; Computer science; Food science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001501279,0.0001905879,0.0001799885,0.002055881,0.007314433,0.003984537,0.0005483441,0.0006359369,0.00322095],"category_scores_gemma":[0.001919993,0.0001728027,0.000188881,0.002121683,0.02598758,0.003876994,0.006437102,0.000928645,0.00008685606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006328862,"about_ca_system_score_gemma":0.002790882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0769877,"about_ca_topic_score_gemma":0.1305226,"domain_scores_codex":[0.9988186,0.0005941718,0.00002758924,0.00009890589,0.0001492516,0.000311458],"domain_scores_gemma":[0.9987749,0.000464556,0.0003493864,0.0001658072,0.00009593771,0.0001493875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00003441299,0.00001456027,0.03519975,0.00004017796,0.00001617574,0.0008864325,0.2592376,0.0005654678,0.0005352623,0.6888396,0.0008169322,0.01381372],"study_design_scores_gemma":[0.00001201447,0.00005279691,0.1365826,0.0002601867,0.00003383117,0.0007865948,0.5726932,0.002855708,0.0007314424,0.1653029,0.1206171,0.00007171189],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9183316,0.0007155052,0.004492548,0.002437307,0.00001732951,0.00001909723,0.00005380807,0.00001249386,0.07392041],"genre_scores_gemma":[0.9987047,0.0001219626,0.0002756526,0.00004840735,0.000002899788,0.000005540006,0.00000997089,0.000003078242,0.0008279168],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0769877,"threshold_uncertainty_score":0.1530792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679511784083761,"score_gpt":0.291199112251648,"score_spread":0.2744039944108104,"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."}}