Introduction to Environmental Data Science Test 1

Vocabulary practice flashcards covering fundamental concepts in environmental data science, data types, spreadsheet capabilities, machine learning models, and AI ethics.

Data Science

Using scientific methods, statistics, computing, and other tools to collect, process, analyze, and interpret data to gain useful information.

Environmental Data

Observations or measurements about the natural environment, such as rainfall, temperature, streamflow, air quality, tree diameter, species, soil moisture, and imagery.

Interpreted Data

Data processing results that explain what raw measurements mean.

System Understanding

Knowledge that explains how or why an environmental system behaves as observed.

Database Management

The processes including storing, organizing, retrieving, updating, protecting, and maintaining data in an organized collection.

Remote Sensing

Collecting information from a distance without direct physical contact, such as with satellites, aircraft, sensors, or drones.

GIS

Geographic Information System: technology used to store, manage, analyze, and display geographically referenced data.

USGS Stream Gage

A monitoring station that measures stream or river conditions, including gage height and streamflow/discharge.

Nominal Data

Categories with no meaningful order, such as species, county name, or land-cover type.

Ordinal Data

Categories or ranks with a meaningful order, but unequal or unknown spacing, such as low/medium/high or 1st/2nd/3rd.

Interval Data

Numeric data with equal intervals but no absolute zero, such as Celsius temperature.

Ratio Data

Numeric data with equal intervals and a true/absolute zero, such as tree diameter, rainfall, or distance.

Absolute Zero

A baseline value indicating that none of the measured quantity exists.

Arbitrary Zero

A reference point on a measurement scale that does not represent the complete absence of a quantity.

Byte

A unit of digital storage equal to 8 bits.

Floating Point

Computer representation for numbers with fractional components.

Vector Model

Represents discrete geographic features with points, lines/arcs, and polygons.

Raster Model

Represents space as a grid of cells/pixels, useful for continuous phenomena such as elevation, temperature, precipitation, and imagery.

Hydrograph

A graph showing stream discharge/flow or stage as it changes over time.

Relative Reference

A spreadsheet cell reference that changes relative to the new location when a formula is copied.

Absolute Reference

A spreadsheet cell reference that uses dollar signs to lock a row/column reference when copied.

CSV

Comma-Separated Values: a plain-text format in which fields are separated by commas.

PivotTable

A spreadsheet tool that summarizes a large table into a compact table by grouping categories and calculating values.

Slicer

An interactive visual control used to filter a PivotTable.

PivotChart

A chart linked to a PivotTable that updates with PivotTable filtering and changes.

Artificial Intelligence (AI)

The broad field of systems performing tasks associated with intelligent behavior.

Machine Learning

A branch of AI in which systems learn patterns from data rather than relying only on explicitly programmed rules.

Deep Learning

A type of machine learning based on multilayer neural networks.

LLM

Large Language Model: a large neural network trained on language data to predict and generate sequences of tokens/text.

Markov Chain

A simple statistical model in which the next state is predicted from the current/recent state.

Transformer

A neural-network architecture using attention to weigh relevant parts of context when producing predictions.

Decision Tree

A model that makes predictions through a sequence of feature-based splits or decisions.

Random Forest

An ensemble of many decision trees whose outputs are combined.

Jagged Capability

A characteristic of AI where it performs extremely well on one task yet fails unexpectedly on a seemingly similar task.

Algorithmic Bias

Systematic errors in model outputs caused by bias in training data or system design.

Duty to Warn

An ethical tension between protecting user privacy and acting to protect others/public safety from serious harm.

Watermarking

Embeds or associates a detectable signal with generated content to help identify its origin, such as SynthID.

Provenance / C2PA

Records and verifies information about digital content's origin and history, differing from an embedded watermark signal.

Delegation

Deciding which parts of a task are appropriate to give to AI.

Description

Providing clear instructions, context, requirements, and constraints to an AI system.

Discernment

Critically evaluating whether AI output is useful, appropriate, and correct.

Diligence

Verifying facts, calculations, citations, attribution, and requirements before using or submitting AI-assisted work.