{"id":"W4285395467","doi":"10.3390/su14148561","title":"The Scope for Using Proximal Soil Sensing by the Farmers of India","year":2022,"lang":"en","type":"article","venue":"Sustainability","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Cropping; Agriculture; Precision agriculture; Agricultural engineering; Environmental science; Scope (computer science); Soil map; Profit (economics); Agricultural productivity; Environmental resource management; Soil water; Computer science; Soil science; Geography; Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.001247355,0.00006793589,0.00007623061,0.000007099351,0.001121871,0.00002356068,0.0001934083,0.00001363556,0.0000465652],"category_scores_gemma":[0.0005300283,0.00004480347,0.00004564834,0.0001787484,0.0004062977,0.00003568411,0.0003728204,0.0001074641,3.741573e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006542781,"about_ca_system_score_gemma":0.0001061253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001477636,"about_ca_topic_score_gemma":0.00004445365,"domain_scores_codex":[0.9990311,0.0001294789,0.0001627685,0.0001787802,0.0002242111,0.0002736197],"domain_scores_gemma":[0.9992937,0.0002947897,0.00009612989,0.0002481318,0.00004029638,0.00002697432],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006347568,0.0004353726,0.1695064,0.0004308007,0.0001105902,0.0000121833,0.01275566,0.05486107,0.009204171,0.0149746,0.02067816,0.7163962],"study_design_scores_gemma":[0.002159582,0.0008485967,0.1658063,0.00001564394,0.00017192,0.00003899674,0.1289068,0.1432389,0.006758794,0.2333097,0.3176363,0.001108418],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934481,0.00008150945,0.003856109,0.0009155223,0.0001115725,0.0008443221,0.00003799665,0.00000986653,0.0006950319],"genre_scores_gemma":[0.9991708,0.000001371231,0.0004836964,0.00008333713,0.000009686021,0.00002815607,0.000003503773,0.000007299807,0.0002120812],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7152878,"threshold_uncertainty_score":0.8628638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00866112965825084,"score_gpt":0.2512899570492235,"score_spread":0.2426288273909727,"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."}}