WIRED36%

NASA’s New 3D Model Shows the Earth Is a Lumpy Mess 12%

By Javier Carbajal24%

7/29/2026, 2:30:00 AM

BS Summary: This article contains 15 faulty reasoning types, including Biased Writer Voice, Appeal to Authority, and Loss Aversion, with Overconfidence Bias as the most egregious example at 13.4% saturation with 52 hits. Analysis detected 356 faulty-reasoning hits from 387 analyzed words, generating a BS Score of 22.9% and a BS Rank of 12% (24,054 of 27,259 articles). This article is better (less manipulative) than 88.20% of the article peer group.

NASA has released a new geoid model showing the shape of Earth’s gravitational field. 
The slight, irregular bulges and depressions are caused by variations in the distribution of mass within the planet. 
It can also be imagined as the surface a global ocean would take if it were determined solely by Earth’s own gravity and rotation. 
The visualization is an exaggerated geoid, with heights multiplied by a factor of 10,000 in order to show the variations in the gravitational field. 
NASA created this lumpy version of Earth using models and data from satellites, which can detect minute differences in gravity at different locations. 
The dataset stretches back 15 years and includes a billion observations. 
NASA also shared an interactive 3D model of the geoid that you can explore here, as well as a longer video. 
Measuring gravity has value beyond creating jarring visuals that’ll make you question just how round Earth is. 
NASA and the European Space Agency both have satellites that keep tabs on the planet’s gravitational field. 
These observations have been used to get a better handle on how much ice Antarctica and Greenland are losing as the planet warms. 
NASA’s satellites—which operate as part of the Gravity Recovery and Climate Experiment (GRACE)—show that Antarctica has lost approximately 135 gigatons of ice per year between 2002 and 2025, while Greenland has lost 264 gigatons of ice annually over that same period. 
Improving those estimates is key to understanding how much oceans are rising now—and how much more they’ll rise in the future. 
The GRACE observations have also been used to track groundwater loss and drought around the world. 
The data shows long-term groundwater depletion in the western US and elsewhere. 
It also provides information on more acute droughts: The extreme dryness that’s fueling fires in Spain and France this summer is visible in GRACE’s data. 
An analysis from the University of Nebraska using gravitational observations shows that soil moisture and shallow groundwater are both sitting in or near record-low territory in parts of the region. 
With climate change set to make drought worse in many locations, these types of observations can provide valuable information to water managers in parched regions. 
This story originally appeared on WIRED en Español and has been translated from Spanish. 
Article reasoning-pattern comparisonThis article: 5.4%Javier Carbajal: 0.6%WIRED: 1.6%Confirmation Bias5.4%This article: 6.2%Javier Carbajal: 0.3%WIRED: 0.6%Anchoring Bias6.2%This article: 4.4%Javier Carbajal: 1.7%WIRED: 2.6%Availability Heuristic4.4%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.7%Representativeness Heuristic0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.8%Hindsight Bias0.0%This article: 13.4%Javier Carbajal: 0.9%WIRED: 1.3%Overconfidence Bias13.4%This article: 2.8%Javier Carbajal: 3.2%WIRED: 3.5%Framing Effect2.8%This article: 6.5%Javier Carbajal: 0.3%WIRED: 0.4%Loss Aversion6.5%This article: 0.0%Javier Carbajal: 0.7%WIRED: 0.4%Status Quo Bias0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.1%Sunk Cost Effect0.0%This article: 0.0%Javier Carbajal: 1.7%WIRED: 1.9%Optimism Bias0.0%This article: 6.5%Javier Carbajal: 1.8%WIRED: 1.2%Pessimism Bias6.5%This article: 2.8%Javier Carbajal: 3.1%WIRED: 5.1%Negativity Bias2.8%This article: 0.0%Javier Carbajal: 2.5%WIRED: 1.1%Self-Serving Bias0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.7%Fundamental Attribution Error0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.2%Actor-Observer Bias0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.8%In-Group Bias0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.2%Out-Group Homogeneity Bias0.0%This article: 0.0%Javier Carbajal: 0.5%WIRED: 1.6%Halo Effect0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.0%Horn Effect0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.0%Dunning-Kruger Effect0.0%This article: 6.5%Javier Carbajal: 0.6%WIRED: 0.8%Recency Bias6.5%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.2%Primacy Effect0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.1%Blind-Spot Bias0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.3%Ad Hominem0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.2%Straw Man0.0%This article: 7.8%Javier Carbajal: 1.9%WIRED: 2.7%Appeal to Authority7.8%This article: 0.0%Javier Carbajal: 0.0%WIRED: 1.0%False Dilemma0.0%This article: 0.0%Javier Carbajal: 1.3%WIRED: 0.5%Slippery Slope0.0%This article: 0.0%Javier Carbajal: 0.5%WIRED: 0.1%Circular Reasoning0.0%This article: 0.0%Javier Carbajal: 0.6%WIRED: 3.8%Hasty Generalization0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.1%Red Herring0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.4%Bandwagon0.0%This article: 4.4%Javier Carbajal: 3.6%WIRED: 2.4%Appeal to Emotion4.4%This article: 0.0%Javier Carbajal: 0.2%WIRED: 0.3%Begging the Question0.0%This article: 6.5%Javier Carbajal: 0.3%WIRED: 2.1%Post Hoc (False Cause)6.5%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.0%Tu Quoque0.0%This article: 0.0%Javier Carbajal: 0.9%WIRED: 0.3%Burden of Proof0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.2%Appeal to Nature0.0%This article: 0.0%Javier Carbajal: 0.1%WIRED: 0.3%Composition/Division0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 2.9%Anecdotal0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.1%No True Scotsman0.0%This article: 2.8%Javier Carbajal: 0.5%WIRED: 1.2%Ambiguity (Equivocation)2.8%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.1%Middle Ground0.0%This article: 0.0%Javier Carbajal: 0.2%WIRED: 0.1%Personal Incredulity0.0%This article: 0.0%Javier Carbajal: 0.2%WIRED: 0.1%Special Pleading0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.1%Genetic Fallacy0.0%This article: 0.0%Javier Carbajal: 1.4%WIRED: 1.0%Unattributed Quote0.0%This article: 0.0%Javier Carbajal: 0.4%WIRED: 0.5%Quote-first Misdirection0.0%This article: 10.6%Javier Carbajal: 2.7%WIRED: 3.2%Biased Writer Voice10.6%This article: 0.0%Javier Carbajal: 1.9%WIRED: 0.8%Indoctrination0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.3%Politically Left Leaning Bias0.0%This article: 0.0%Javier Carbajal: 0.0%WIRED: 0.1%Politically Right Leaning Bias0.0%This article: 5.4%Javier Carbajal: 1.6%WIRED: 1.7%Attempt to Sell a Product or S…5.4%

387 words analyzed.

Speakers

3speakers38%attributed speech241writer words
100%flagged-word coverage
30 attributed words21% of attributed speech56% writer coverage
0%10.0%20.0%Biased Writer Voice-17.0 ptsWriter: 17.0%University of Nebraska: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

Loading…
Loading…
Loading…
Loading…

Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.