Quantitative Reasoning and the Environment: Mathematical Modeling in Context
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,025 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,004 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,018 |
| Communication savante | 0,011 | 0,016 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».