After rivers flood, King County Water Taxis use infrared goggles to dodge logs⁠71%

By Casey Martin⁠0%

12/19/2025, 5:11:21 PM

BS Summary: This article contains 12 faulty reasoning types, including Framing Effect, Negativity Bias, and Availability Heuristic, with Optimism Bias as the most egregious example at 30.2% saturation with 107 hits. Analysis detected 320 faulty-reasoning hits from 354 analyzed words, generating a BS Score of 63.8% and a BS Rank of ⁠71% (6,375 of 21,887 articles). This article is worse (more manipulative) than 70.90% of the article peer group.

The Vashon Island water taxi about to set sail on Friday, Oct. 10, 2025. 
Seattle was spared from the worst of recent flooding, but the raging rivers in Western Washington sent lots of trees and logs into Puget Sound. 
That can mean trouble for captains sailing through Elliott Bay. 
Crews aboard King County's water taxis are used to some debris floating in the water this time of year. 
But as December 2025 has seen record-setting river flooding, it's been worse. 
Heavy rain and wind, like we’ve seen the past couple of weeks, can push a lot of the trees, logs, and debris along river banks into waterways. 
RELATED: First came the rain. 
Then came the toilet rats. 
That can be a hazard for boats, like King County Metro’s Water Taxis that crisscross Elliott Bay. 
Aboard every taxi is a crewmember dedicated as a lookout to keep an eye on the water to make sure there isn't any debris in front of them. 
To avoid crashing into a big, floating log, Terry Federer from King County Metro says they use tools straight out of a spy movie: infrared goggles. 
"The debris in the water has a different heat signature than the water itself,” Federer told KUOW. 
“So, with the infrared goggles, the lookout can spot different types of debris and then tell the captain that they should move to this direction or to that direction to change course." 
The boats have light bars on them, too, that emit infrared light. 
Federer said they can not only spot logs with the goggles and lights, but also large animals like whales. 
Water taxis are nimble enough, he says, to quickly sail around obstacles. 
Because of their catamaran shape, captains can also sail over a piece of debris with the object slipping in between the two hulls. 
RELATED: How much water flooded parts of Western Washington? At least 3 Lake Washingtons' worth 
This time of year, with limited visibility and more stuff in the water, taxis will sometimes slow down a few knots. 
Thankfully, so far this month, that has not caused big delays to service, Federer said. 
Article reasoning-pattern comparisonThis article: 0.0%Casey Martin: 0.0%KUOW: 0.2%Actor-Observer Bias0.0%This article: 4.2%Casey Martin: 1.3%KUOW: 1.3%Anchoring Bias4.2%This article: 7.6%Casey Martin: 3.6%KUOW: 3.4%Availability Heuristic7.6%This article: 0.0%Casey Martin: 0.0%KUOW: 0.0%Blind-Spot Bias0.0%This article: 0.0%Casey Martin: 2.2%KUOW: 2.6%Confirmation Bias0.0%This article: 0.0%Casey Martin: 0.0%KUOW: 0.0%Dunning-Kruger Effect0.0%This article: 13.0%Casey Martin: 8.5%KUOW: 7.4%Framing Effect13.0%This article: 0.0%Casey Martin: 0.5%KUOW: 0.9%Fundamental Attribution Error0.0%This article: 5.4%Casey Martin: 3.9%KUOW: 2.7%Halo Effect5.4%This article: 0.0%Casey Martin: 0.7%KUOW: 0.7%Hindsight Bias0.0%This article: 0.0%Casey Martin: 0.0%KUOW: 0.2%Horn Effect0.0%This article: 0.0%Casey Martin: 6.1%KUOW: 2.0%In-Group Bias0.0%This article: 0.0%Casey Martin: 3.4%KUOW: 1.0%Loss Aversion0.0%This article: 9.0%Casey Martin: 7.7%KUOW: 8.0%Negativity Bias9.0%This article: 30.2%Casey Martin: 5.2%KUOW: 3.8%Optimism Bias30.2%This article: 0.0%Casey Martin: 1.0%KUOW: 0.5%Out-Group Homogeneity Bias0.0%This article: 0.0%Casey Martin: 2.2%KUOW: 1.4%Overconfidence Bias0.0%This article: 2.8%Casey Martin: 1.4%KUOW: 1.8%Pessimism Bias2.8%This article: 0.0%Casey Martin: 0.2%KUOW: 0.4%Primacy Effect0.0%This article: 3.4%Casey Martin: 1.7%KUOW: 1.1%Recency Bias3.4%This article: 0.0%Casey Martin: 1.2%KUOW: 1.2%Representativeness Heuristic0.0%This article: 0.0%Casey Martin: 1.4%KUOW: 2.0%Self-Serving Bias0.0%This article: 4.2%Casey Martin: 1.9%KUOW: 1.0%Status Quo Bias4.2%This article: 0.0%Casey Martin: 0.2%KUOW: 0.2%Sunk Cost Effect0.0%This article: 0.0%Casey Martin: 0.1%KUOW: 0.5%Ad Hominem0.0%This article: 0.0%Casey Martin: 1.5%KUOW: 1.4%Ambiguity (Equivocation)0.0%This article: 0.0%Casey Martin: 5.6%KUOW: 3.3%Anecdotal0.0%This article: 0.0%Casey Martin: 1.8%KUOW: 4.3%Appeal to Authority0.0%This article: 1.4%Casey Martin: 9.1%KUOW: 6.1%Appeal to Emotion1.4%This article: 0.0%Casey Martin: 0.1%KUOW: 0.2%Appeal to Nature0.0%This article: 0.0%Casey Martin: 0.9%KUOW: 0.8%Bandwagon0.0%This article: 0.0%Casey Martin: 0.2%KUOW: 0.8%Begging the Question0.0%This article: 0.0%Casey Martin: 0.2%KUOW: 0.3%Burden of Proof0.0%This article: 0.0%Casey Martin: 0.2%KUOW: 0.1%Circular Reasoning0.0%This article: 0.0%Casey Martin: 0.0%KUOW: 0.2%Composition/Division0.0%This article: 0.0%Casey Martin: 1.1%KUOW: 1.4%False Dilemma0.0%This article: 0.0%Casey Martin: 0.0%KUOW: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Casey Martin: 0.0%KUOW: 0.2%Genetic Fallacy0.0%This article: 0.0%Casey Martin: 4.4%KUOW: 4.1%Hasty Generalization0.0%This article: 0.0%Casey Martin: 0.1%KUOW: 0.1%Middle Ground0.0%This article: 0.0%Casey Martin: 0.0%KUOW: 0.1%No True Scotsman0.0%This article: 0.0%Casey Martin: 0.0%KUOW: 0.1%Personal Incredulity0.0%This article: 7.6%Casey Martin: 1.5%KUOW: 2.2%Post Hoc (False Cause)7.6%This article: 1.4%Casey Martin: 0.1%KUOW: 0.3%Red Herring1.4%This article: 0.0%Casey Martin: 0.3%KUOW: 0.9%Slippery Slope0.0%This article: 0.0%Casey Martin: 0.0%KUOW: 0.2%Special Pleading0.0%This article: 0.0%Casey Martin: 0.1%KUOW: 0.3%Straw Man0.0%This article: 0.0%Casey Martin: 0.0%KUOW: 0.1%Tu Quoque0.0%

354 words analyzed.

Speakers

No attributed speakers were identified in this analysis.

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Analysis

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