Quantitative Reasoning and the Environment: Mathematical Modeling in Context
Bibliographic record
Abstract
Part 1: Essential Numeracy Chapter 1: Measurement and Units Mercury and the Inuit of Greenland Measuring Accuracy and Precision of Measurement Estimation and Approximation Units of Measurement Unit Conversion Compound Units Units in Equations and Formulas Unit Prefixes Scientific Notation and Order of Magnitude Powers of 10 and Logarithms Logarithmic Scales Chapter Summary End-of-Chapter Exercises Science in Depth: Global Warming Chapter Project: Melting of the Ice Caps (all projects posted at enviromath.com) Chapter 2: Ratios and Percentages Ratios Normalization Percentage as a Type of Ratio Parts per Thousand Parts per Million and Parts per Billion Percentage as a Measure of Change Percentage Difference and Percentage Error Proportions Probability Recurrence Interval Chapter Summary End-of-Chapter Exercises Science in Depth: Sinkholes and Lakes Chapter Project: Measuring Habitat of Florida Lakes Chapter 3: Charts and Graphs Pie Charts Bar Charts Frequency Histograms Using Technology: Histograms Relative Frequency Histograms Scatterplots Using Technology: Scatterplots Line Graphs Chapter Summary End-of-Chapter Exercises Science in Depth: Energy Demand and the Arctic National Wildlife Refuge Chapter Project: U.S.Energy Flows Part 2: Function Modeling Chapter 4: Linear Functions and Regression Modeling with Linear Functions Units of Measure in Linear Equations Dependent versus Independent Variables Graphing Linear Equations Using Technology: Graphs and Tables Approximating Almost-Linear Data Sets straightedge method least squares regression Using Technology: Linear Regression The Correlation Coefficient r Using Technology: The Correlation Coefficient Correlation Fallacies Chapter Summary End-of-Chapter Exercises Science in Depth: Population Growth Chapter Project:Fertility Rates in Developing Countries Chapter 5: Exponential Functions and Regression Exponential Rates and Multipliers The General Exponential Model Finding Exponential Functions-the More General Case Solving Exponential Equations Doubling Times and Half-Lives Approximating Almost-Exponential Data Sets straightedge method least squares regression Using Technology: Exponential Regression Chapter Summary End-of-Chapter Exercises Science in Depth: Chicken Nation Chapter Project:Broiler Chicken Production in the United States Chapter 6: Power Functions Basic Power Functions Solving Power Equations Approximating Power-Like Data Sets straightedge method least squares regression Using Technology: Power Regression Power Law Frequency Distributions Power Law Distributions and Fractals Recurrence Intervals Chapter Summary End-of-Chapter Exercises Science in Depth: Earthquakes and Fractals Chapter Project:A New Model for Earthquakes Part3: Difference Equation Modeling Chapter 7: Introduction to Difference Equations Sequences and Notation Modeling with Difference Equations Linear Difference Equations Exponential Difference Equations Why Use Difference Equations? Affine Difference Equations Using Technology: Difference Equations Chapter Summary End-of-Chapter Exercises Science in Depth: The Politics of Immigration Chapter Project:Human Population and Migration Chapter 8: Affine Solution Equations and Equilibrium Values The Solution Equation to the Affine Model Equilibrium Values Classification of Equilibrium values Revisiting the Affine Solution Equation Chapter Summary End-of-Chapter Exercises Science in Depth: Get The Lead Out Chapter Project:Lead in the Body Chapter 9: Logistic Growth, Harvesting and Chaos Modeling Logistic Growth with Difference Equations Logistic Equilibrium Values Harvest Models Periodic Behavior Chaotic Behavior Chapter Summary End-of-Chapter Exercises Science in Depth: Harvesting and Sustainable Forestry Chapter Project: Harvesting and Sustainability Chapter 10: Systems of Difference Equations Systems Modeling Using Technology: Systems of Difference Equations Exponential Change and Stable Age Distributions What Else Besides Populations? Chapter Summary End-of-Chapter Exercises Science in Depth: A River Runs Through Europe Chapter Project:Water Pollution in a System of Lakes Part 4: Elementary Statistics Chapter 11: Fundamentals of Statistics Measures of Center and Other Descriptive Statistics Weighted Means Quartiles and the 5 Number Summary Boxplots Using Technology: Finding Descriptive Statistics Shape of a Data Set Using Technology: Histograms A Skew Formula Comparing the Mean and Median Sampling Chapter Summary End-of-Chapter Exercises Science in Depth: Energy Makeover Chapter Projects: Electric Bills and Super Bulbs Chapter 12: Standard Deviation Standard Deviation Calculating Position Using Z-scores Outliers Chebychev's Rule Normal Distributions The Empirical Rule Chapter Summary End-of-Chapter Exercises Science in Depth: Impermeable Surfaces and Urban Runoff Chapter Projects: Urban Runoff Index Chapter 13: Normal Distributions The Standard Normal Distribution Transformations to Normal Confidence Intervals Chapter Summary End-of-Chapter Exercises Science in Depth: Hazardous Household Waste Chapter Project:Hazardous Waste Generation and Monetary Disincentives Appendix: Common Units of Measurement Solutions to Odd Exercises Index
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.018 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".