Superworms could replace beetles for cleaning skeletal remains 11%

By Jennifer Ouellette10%

7/1/2026, 6:59:21 PM

BS Summary: This article contains 20 faulty reasoning types, including Hasty Generalization, Confirmation Bias, and Negativity Bias, with Overconfidence Bias as the most egregious example at 15.7% saturation with 96 hits. Analysis detected 832 faulty-reasoning hits from 612 analyzed words, generating a BS Score of 28% and a BS Rank of 11% (19,514 of 21,887 articles). This article is better (less manipulative) than 89.20% of the article peer group.

Preparing skeletal specimens for display in museums or for forensic studies requires the bones to be thoroughly cleaned to remove any remaining flesh or soft tissue. 
However, the need for thorough cleaning must be balanced against the risk of damaging the actual bones. 
According to a new paper published in the journal PLoS One, the larvae of so-called “superworms” (Zophobas morio)—a common pet food—offer a practical alternative. 
There are existing methods for cleaning skeletal remains, such as burial, digestive enzymes, or chemical treatments. 
But most have drawbacks, including damaging bones, taking a long time to process, having expensive operational costs, or the use of environmentally hazardous substances. 
Using dermestid beetles has become the preferred method for skeletal cleaning since they can efficiently remove soft tissue without damaging the bone. 
The downside is that without strict containment practices, the beetles can escape and lay eggs that hatch, leading to infestations that threaten museum collections. 
Fatemah Rastekar of Ferdowsi University of Mashhad in Iran and co-authors thought superworms might bring the same benefits as the beetles without the risk of infestation. 
For one thing, beetle colonies span all life stages and hence require complex containment; superworm cleaning only requires the larval stage, which lasts 10–12 weeks compared to just five to seven weeks for the beetles. 
And the larvae don’t pupate in crowded conditions, so it’s easier to manage the colonies while reducing the risk of escape. 
But could superworms match the cleaning efficiency of their rival beetles? 
As the worm turns 
To find out, Rastekar et al. collected several donated specimens of various sizes and species and cleaned them using commercially available superworms: an Egyptian rosette, a house mouse, a little bittern, an alligator gar, a Eurasian eagle-owl, a rook, a wild cat, and a gray wolf. 
They also performed a parallel experiment for comparison, cleaning the skeleton of a marbled polecat using a conventional boiling method to remove the flesh. 
All the specimens were skinned first, and the team removed any excess flesh and internal organs. 
Each specimen was weighed and put in the same-sized containers filled with superworm larvae to determine the optimal ratio of larvae to specimen for thorough cleaning without damaging bones. 
The team rotated larger specimens every six to eight hours into fresh containers. 
After each cleaning session, the larvae were fed fruit or vegetable peels, since feeding only on flesh can prevent the superworms from molting or even hasten their death. 
Any waste materials were removed regularly to maintain hygienic conditions. 
Once the larvae were done chowing down, the skeletons were removed from the containers and rinsed with warm water to remove any residual larvae or tissue. 
While they did briefly immerse the skeletons in a 1 percent bleach solution, the authors cautioned that this can damage bone tissue, so it’s not recommended. 
Finally, the skeletons were coated with a clear gloss varnish spray to prepare them for display. 
(This step would be skipped in a forensic setting, since varnish sprays are not ideal for things like CT analysis.) 
The result: A ratio of 10 to 15 grams of larvae per gram of animal specimen proved the most optimal, minimizing cleaning time while not damaging any bones. 
Once the optimal ratio had been established, Rastekar et al. conducted follow-up tests on three small bird skulls, with similar results. 
The authors recommend using larger containers for medium to large specimens to reduce cleaning time and the need to reposition them frequently. 
“Altogether, these findings demonstrate that superworms provide an adaptable and effective alternative for skeletal preparation in museum and research settings,” they concluded. 
PLoS One, 2026. 
DOI: 10.1371/journal.pone.0349669 (About DOIs). 
Article reasoning-pattern comparisonThis article: 13.4%Jennifer Ouellette: 2.2%Ars Technica: 2.8%Confirmation Bias13.4%This article: 5.7%Jennifer Ouellette: 0.5%Ars Technica: 1.2%Anchoring Bias5.7%This article: 7.5%Jennifer Ouellette: 2.3%Ars Technica: 2.4%Availability Heuristic7.5%This article: 0.0%Jennifer Ouellette: 1.0%Ars Technica: 1.0%Representativeness Heuristic0.0%This article: 0.0%Jennifer Ouellette: 0.4%Ars Technica: 0.6%Hindsight Bias0.0%This article: 15.7%Jennifer Ouellette: 1.3%Ars Technica: 2.1%Overconfidence Bias15.7%This article: 4.6%Jennifer Ouellette: 2.5%Ars Technica: 3.4%Framing Effect4.6%This article: 0.0%Jennifer Ouellette: 0.1%Ars Technica: 0.5%Loss Aversion0.0%This article: 3.6%Jennifer Ouellette: 0.4%Ars Technica: 0.5%Status Quo Bias3.6%This article: 0.0%Jennifer Ouellette: 0.0%Ars Technica: 0.1%Sunk Cost Effect0.0%This article: 4.2%Jennifer Ouellette: 2.6%Ars Technica: 4.2%Optimism Bias4.2%This article: 4.7%Jennifer Ouellette: 1.1%Ars Technica: 1.7%Pessimism Bias4.7%This article: 12.1%Jennifer Ouellette: 8.2%Ars Technica: 6.2%Negativity Bias12.1%This article: 3.6%Jennifer Ouellette: 1.3%Ars Technica: 1.2%Self-Serving Bias3.6%This article: 0.0%Jennifer Ouellette: 0.2%Ars Technica: 0.6%Fundamental Attribution Error0.0%This article: 0.0%Jennifer Ouellette: 0.0%Ars Technica: 0.1%Actor-Observer Bias0.0%This article: 0.0%Jennifer Ouellette: 0.3%Ars Technica: 0.6%In-Group Bias0.0%This article: 0.0%Jennifer Ouellette: 0.0%Ars Technica: 0.2%Out-Group Homogeneity Bias0.0%This article: 3.6%Jennifer Ouellette: 1.6%Ars Technica: 2.0%Halo Effect3.6%This article: 0.0%Jennifer Ouellette: 0.0%Ars Technica: 0.1%Horn Effect0.0%This article: 0.0%Jennifer Ouellette: 0.0%Ars Technica: 0.0%Dunning-Kruger Effect0.0%This article: 3.4%Jennifer Ouellette: 0.8%Ars Technica: 1.0%Recency Bias3.4%This article: 0.0%Jennifer Ouellette: 0.5%Ars Technica: 0.3%Primacy Effect0.0%This article: 0.0%Jennifer Ouellette: 0.1%Ars Technica: 0.1%Blind-Spot Bias0.0%This article: 0.0%Jennifer Ouellette: 0.2%Ars Technica: 0.5%Ad Hominem0.0%This article: 0.0%Jennifer Ouellette: 0.5%Ars Technica: 0.2%Straw Man0.0%This article: 7.5%Jennifer Ouellette: 1.3%Ars Technica: 4.1%Appeal to Authority7.5%This article: 7.0%Jennifer Ouellette: 0.8%Ars Technica: 1.1%False Dilemma7.0%This article: 3.9%Jennifer Ouellette: 1.0%Ars Technica: 0.7%Slippery Slope3.9%This article: 0.0%Jennifer Ouellette: 0.1%Ars Technica: 0.1%Circular Reasoning0.0%This article: 15.5%Jennifer Ouellette: 5.0%Ars Technica: 3.8%Hasty Generalization15.5%This article: 0.0%Jennifer Ouellette: 0.5%Ars Technica: 0.2%Red Herring0.0%This article: 0.0%Jennifer Ouellette: 1.8%Ars Technica: 0.6%Bandwagon0.0%This article: 0.0%Jennifer Ouellette: 3.2%Ars Technica: 2.7%Appeal to Emotion0.0%This article: 0.0%Jennifer Ouellette: 0.8%Ars Technica: 0.6%Begging the Question0.0%This article: 8.0%Jennifer Ouellette: 1.7%Ars Technica: 2.3%Post Hoc (False Cause)8.0%This article: 0.0%Jennifer Ouellette: 0.0%Ars Technica: 0.2%Tu Quoque0.0%This article: 0.0%Jennifer Ouellette: 0.3%Ars Technica: 0.5%Burden of Proof0.0%This article: 3.6%Jennifer Ouellette: 0.1%Ars Technica: 0.2%Appeal to Nature3.6%This article: 0.0%Jennifer Ouellette: 0.0%Ars Technica: 0.2%Composition/Division0.0%This article: 7.5%Jennifer Ouellette: 0.9%Ars Technica: 1.5%Anecdotal7.5%This article: 0.0%Jennifer Ouellette: 0.0%Ars Technica: 0.1%No True Scotsman0.0%This article: 0.0%Jennifer Ouellette: 0.9%Ars Technica: 1.7%Ambiguity (Equivocation)0.0%This article: 0.0%Jennifer Ouellette: 0.0%Ars Technica: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Jennifer Ouellette: 0.1%Ars Technica: 0.1%Middle Ground0.0%This article: 0.0%Jennifer Ouellette: 0.0%Ars Technica: 0.1%Personal Incredulity0.0%This article: 0.0%Jennifer Ouellette: 0.3%Ars Technica: 0.1%Special Pleading0.0%This article: 0.0%Jennifer Ouellette: 0.0%Ars Technica: 0.1%Genetic Fallacy0.0%This article: 0.0%Jennifer Ouellette: 1.1%Ars Technica: 1.5%Unattributed Quote0.0%This article: 0.0%Jennifer Ouellette: 0.7%Ars Technica: 1.0%Quote-first Misdirection0.0%This article: 0.7%Jennifer Ouellette: 5.5%Ars Technica: 4.5%Biased Writer Voice0.7%This article: 0.0%Jennifer Ouellette: 0.5%Ars Technica: 1.0%Indoctrination0.0%This article: 0.0%Jennifer Ouellette: 0.6%Ars Technica: 0.7%Politically Left Leaning Bias0.0%This article: 0.0%Jennifer Ouellette: 0.0%Ars Technica: 0.2%Politically Right Leaning Bias0.0%This article: 0.0%Jennifer Ouellette: 0.6%Ars Technica: 1.3%Attempt to Sell a Product or S…0.0%

612 words analyzed.

Speakers

2speakers8.7%attributed speech559writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 8 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 17 words • 0.0% coveragePLoS One • 24 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageFatemah Rastekar • 26 words • 0.0% coverageWriter's voice • 35 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 4 words • 100.0% coverageWriter's voice • 46 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 29 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 22 words • 0.0% coveragePLoS One • 3 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverage
Selected voice

Fatemah Rastekar

100%flagged-word coverage
26 attributed words49% of attributed speech63% writer coverage
0%2.5%5.0%Biased Writer Voice-0.7 ptsWriter: 0.7%Fatemah Rastekar: 0.0%0.0%

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

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Analysis

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