environment-life-review-forge

Environment Life Review Forge

Use this skill for environmental, ecological, biomedical, and life-science systematic reviews where exposure, organism/population, outcome, context, and study design need careful domain adaptation.

Core Principle

Domain structure matters. The same effect-size workflow may be misleading if exposure windows, species, tissues, endpoints, geography, or measurement platforms are not comparable.

Intake

Identify:

• domain: environment, ecology, toxicology, epidemiology, life science, molecular biology, public health;
• framework: PECO or PICO;
• population or organism;
• exposure or intervention;
• comparator;
• outcomes/endpoints;
• study design;
• spatial and temporal scale;
• ecosystem-service set, pairwise relationship definition, synergy/trade-off coding, and threshold-management target, if ecosystem-service relationships are in scope;
• pollutant exposure, crop outcome, food-security endpoint, counterfactual air-quality target, and crop-calorie translation, if air-quality food-security modeling is in scope;
• biodiversity dimension, stability metric, climate-stress gradient, and moderation/interaction target, if biodiversity-stability evidence is in scope;
• spatial unit and aggregation boundary, if geospatial prediction is in scope;
• minimum mapping unit and detection threshold, if small-patch systems are in scope;
• target map variable, spatial resolution, observation inventory, predictor stack, spatial autocorrelation plan, and uncertainty layer, if an environmental map product is in scope;
• measurement method;
• bidirectional pathways, if impacts and feedbacks are both in scope;
• expected heterogeneity.

Load:

• references/environmental-life-science.md for domain heterogeneity.
• references/cee-alignment.md for environmental evidence standards.
• references/pls-vip-environmental-indicators.md for NDVI, vegetation, soil, climate, ecological indicator, PLS regression, and VIP interpretation audits.
• references/ecosystem-service-threshold-ml.md for ecosystem-service relationship mapping, GWR-plus-ML workflows, nonlinear driver interpretation, threshold/optimal-interval identification, and spatial management translation.
• references/air-quality-food-security.md for ozone, aerosol, SIF, crop yield, crop-calorie, counterfactual air-quality targets, and food-security co-benefit modeling.
• references/soil-biodiversity-aridity-stability.md for soil biodiversity, aridity gradients, ecosystem stability, climate-stress moderation, and biodiversity-function buffering claims.
• references/ant-soil-carbon-meta.md for soil-fauna meta-analysis, ecosystem-engineer effects on SOC stock and CO2 flux, trait-mediated moderators, and climate-context extraction.
• references/small-wetland-methane-scaling.md for wetland methane, small water bodies, fine-resolution remote sensing, and scale-sensitive upscaling.
• references/cryosphere-ground-ice-mapping.md for permafrost, near-surface ground ice, borehole observations, geospatial predictors, ensemble machine learning, spatial autocorrelation, prediction intervals, and public map-data audits.
• references/agroecosystem-nutrient-meta-analysis.md for crop yield, soil organic carbon, fertilizer, amendment, and nutrient-management meta-analyses.
• references/agricultural-ml-yield-prediction.md for crop-yield prediction studies integrating meteorological, breeding, genomic, remote-sensing, or field-trial data.
• references/agricultural-irrigation-optimization.md for brackish-water irrigation, water-salt-yield-emission trade-offs, GAM nonlinear response modeling, NSGA-II optimization, and decision ranges such as ECw management windows.
• references/environmental-causal-ml.md for environmental causal machine learning studies using DML, CATE, AutoML, SHAP/PDP-style interpretation, high-dimensional pollutant exposure data, socioeconomic covariates, ARGs, drinking-water safety, or One Health outcomes.
• references/food-system-bidirectional-nexus.md for food-system reviews linking environmental pressures, feedbacks, trade, diets, crops, livestock, and aquatic foods.
• references/food-waste-geospatial-ml.md for county, city, supply-chain, or market-level food-waste forecasting with geospatial analytics and machine learning.
• references/environmental-scenario-synthesis.md when a review builds a literature-derived database, machine-learning/spatial model, or policy scenario simulation.
• references/land-use-optimization-tradeoffs.md when a study uses multiobjective optimization, Pareto frontiers, land-use allocation, or food-water-carbon trade-off modeling.
• references/system-hub-policy-synthesis.md when a paper uses one focal variable, such as nitrogen, carbon, water, phosphorus, air pollution, or biodiversity pressure, to connect multiple environmental, production, health, or policy outcomes under a boundary or scenario framework.