{"id":"W2302304515","doi":"10.3390/rs8030226","title":"Examining the Influence of Seasonality, Condition, and Species Composition on Mangrove Leaf Pigment Contents and Laboratory Based Spectroscopy Data","year":2016,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Coastal wetland ecosystem dynamics","field":"Environmental Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"Algoma University; Western University; Nipissing University","funders":"Dirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de México; Instituto de Ciencias del Mar y Limnología, Universidad Nacional Autónoma de México; Universidad Nacional Autónoma de México; Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología","keywords":"Rhizophora mangle; Mangrove; Botany; Dry season; Wet season; Biology; Chlorophyll a; Chlorophyll; Seasonality; Ecology","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":[],"consensus_categories":[],"category_scores_codex":[0.0002814958,0.00009606525,0.0001171037,0.0000136406,0.0001124279,0.0000301554,0.00009359169,0.00002663781,0.0000219476],"category_scores_gemma":[0.00004218515,0.00006212513,0.000007987738,0.00006394659,0.0002290355,0.0001600703,0.0002381958,0.00004756247,0.00001216034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007991063,"about_ca_system_score_gemma":0.000005826176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001025587,"about_ca_topic_score_gemma":0.00015406,"domain_scores_codex":[0.9991544,0.00008624094,0.0001497798,0.0002576708,0.0002183858,0.0001335412],"domain_scores_gemma":[0.9992986,0.0001890746,0.0001077111,0.0003428297,0.00001406366,0.00004766024],"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":[0.00005495622,0.00001413609,0.03463912,0.00003387786,0.00001311993,0.000009803093,0.00009707656,0.0003480033,0.9592618,0.00003691515,0.0001520735,0.005339106],"study_design_scores_gemma":[0.0009543281,0.0001516487,0.8834747,0.0007628733,0.00003600007,0.00002852601,0.000200183,0.08514329,0.02834316,0.0002209971,0.0004406887,0.0002435869],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964724,0.00001350537,0.002347471,0.0004193035,0.00003410257,0.0001429156,0.0001138504,0.00001345421,0.0004430076],"genre_scores_gemma":[0.9986704,0.00001725469,0.001035039,0.0001955883,0.00001760422,1.223585e-8,0.0000132986,0.0000076421,0.00004314563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9309186,"threshold_uncertainty_score":0.2533389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.024111834770314,"score_gpt":0.2381740238627267,"score_spread":0.2140621890924126,"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."}}