Skip to content
Chimera readability score 58 out of 100, Graduate reading level.

This is a human-written story voiced by AI. Got feedback? Take our survey . (See our AI policy here .)
An AI-powered monitoring system could save the lives of gray whales that are increasingly taking a deadly detour into California’s heavily trafficked San Francisco Bay.
The new technology combines round-the-clock thermal cameras deployed at different locations in the bay with AI to detect whales that may be as far as 7 kilometers away. Once the whale detection is confirmed by scientists, an alert goes out to warn vessels in the area to slow down or change course to avoid a collision.
A coalition of ocean scientists, the U.S. Coast Guard, whale tracking experts and local ferry companies unveiled the deployment in the bay on May 19. A camera mounted on a radio tower on Angel Island within the bay will monitor numerous busy shipping routes. A second camera will be installed on a passenger ferry that crosses the bay daily, and future additional camera sites could include the Golden Gate Bridge and Alcatraz.
The whale-detecting AI-powered tech is the brainchild of researchers at the Woods Hole Oceanographic Institution, or WHOI, in Massachusetts, who later created a company called WhaleSpotter to market the tech. “We wanted to be able to detect whales so far out that it would give mariners time to take action,” says Daniel Zitterbart, a physicist at WHOI and the chief scientist of WhaleSpotter. That’s particularly important for large ships, such as container vessels, that have a great deal of inertia and can’t quickly change course.
Developing a reliable whale detection system took about 15 years, Zitterbart says. Water emitted from whales’ blowholes, or the whales’ bodies themselves, is warmer than the ambient water by about 2 degrees Celsius. So the researchers used hundreds of thousands of thermal images to train the AI to recognize those relative temperature differences as signifying a whale. Then, when there’s a detection, a WhaleSpotter researcher will verify the data, to minimize false positives. Once verified, an alert is sent to any vessels nearby.
“We want as many deployments as possible, because that ultimately means we have better eyes on the ocean,” Zitterbart says. “Shipping is not going to disappear. We need to have a tech that allows us to use the ocean, but also allows the whales to go about their lives.”
In 2025, 21 gray whales (Eschrichtius robustus) were found dead in and around San Francisco Bay; two-fifths of those deaths were due to ship strikes, researchers say. The deaths are part of a disturbing trend that researchers first observed in 2018: The whales were increasingly making a pit stop in the bay along their 16,000-kilometer-long migration southward from their feeding grounds off Alaska’s coast to their mating grounds near Mexico.
The whales were likely hungry. In the Arctic, they feed on tiny crustaceans called amphipods in ocean sediments; those amphipods, in turn, are nourished by algae that grows on the underside of sea ice. Climate change is rapidly melting that sea ice, disrupting the food chain.
Gray whale populations declined dramatically from about 20,500 in 2018 to about 14,500 in 2023. Hundreds of whales were found stranded along the North American west coast. Many of those whales were suffering from malnutrition. So, to sustain themselves for the rest of their migration, they have been heading into the bay looking for food.
“It is heartbreaking to see these starving whales stumbling around in the middle of the hustle and bustle of San Francisco Bay,” University of California, Santa Barbara marine ecologist Douglas McCauley said May 19 in a news release. McCauley is the director of UCSB’s Benioff Ocean Science Laboratory, one of the coalition partners that developed and is deploying the new technology. “Every day is a nail-biter.… This new system will save whales’ lives.”
Josephine Slaathaug, a whale biologist at Sonoma State University in Rohnert Park, Calif., says she hopes this technology will be a “huge leap in the right direction to protecting whales in San Francisco Bay.”
“I’m cautiously optimistic,” Slaathaug says. “I’m very glad that the vessel strike issue is being taken seriously.” And it’s especially heartening, she adds, to see so many different organizations and partners — including the shipping industry — working together to develop a science-based, long-term solution.

Facts Only

* An AI-powered monitoring system detects gray whales using round-the-clock thermal cameras and AI.
* The system is designed to alert vessels to slow down or change course to avoid collisions.
* The technology was developed by researchers at the Woods Hole Oceanographic Institution (WHOI) and marketed by WhaleSpotter.
* The deployment involves a coalition of ocean scientists, the U.S. Coast Guard, whale tracking experts, and local ferry companies.
* A camera will be mounted on a radio tower on Angel Island to monitor shipping routes.
* A second camera will be installed on a passenger ferry crossing the bay daily.
* Water emitted by whales is warmer than ambient water by about 2 degrees Celsius.
* Hundreds of thousands of thermal images were used to train the AI to recognize whale signatures.
* Whale detection is verified by a WhaleSpotter researcher to minimize false positives.
* In 2025, 21 gray whales were found dead in and around San Francisco Bay, two-fifths of which were due to ship strikes.
* Gray whale populations declined from about 20,500 in 2018 to about 14,500 in 2023.
* The research was observed in 2018 regarding the whales making a pit stop in the bay along their migration route.

Executive Summary

An AI-powered monitoring system uses round-the-clock thermal cameras and artificial intelligence to detect gray whales in the San Francisco Bay. The system alerts vessels to slow down or change course to prevent collisions. The technology was developed by researchers at the Woods Hole Oceanographic Institution (WHOI) and commercialized by WhaleSpotter. The system utilizes thermal imaging, recognizing temperature differences emitted by whales, and requires verification by researchers to minimize false positives. The deployment involves a coalition of ocean scientists, the U.S. Coast Guard, whale tracking experts, and local ferry companies. The technology addresses the threat of ship strikes, which are cited as the cause of many gray whale deaths in the bay, especially among the 21 whales found dead in 2025. The project addresses the context of declining gray whale populations, which have dropped from about 20,500 in 2018 to about 14,500 in 2023, due in part to climate change affecting their food sources.

Full Take

The narrative effectively frames the solution as a scientifically sound, collaborative technological triumph, which serves to mitigate immediate risk (ship strikes). However, the underlying motivation—saving the whales—is used to drive the adoption of a large-scale technological intervention that involves significant real-world operational changes and stakeholder agreements. The focus on the technology's development (15 years) and the complexity of the data verification (AI training, thermal signatures) lends an air of objective legitimacy, but this complexity can create an avoidance mechanism for deeper systemic questions regarding the relationship between human shipping infrastructure and marine ecological health. The system successfully highlights the tension between maritime commerce and biodiversity preservation. The pattern detected is a reliance on technological intervention as the primary means of addressing environmental harm, which can subtly shift focus away from the root causes of the problem—namely, the climate-driven disruption of the food chain and the economic imperatives driving migration and shipping. The implication is that while technology can manage the symptoms of human-caused environmental degradation, the fundamental systemic conflict between industrial activity and ecological necessity remains unresolved. The system shifts the responsibility for action from abstract moral obligation to concrete, measurable technical feasibility.
Patterns detected: ARC-0043 Motte-and-Bailey, ARC-0024 Ambiguity, ARC-0051 Framing

Sentinel — Human

Confidence

The text functions as high-quality journalistic reporting, successfully blending complex scientific and technological details with emotional context, exhibiting strong human-authored characteristics.

Signals Detected
low severity: Irregular sentence length variance and natural flow; contains idiosyncratic emotional emphasis (e.g., 'heartbreaking,' 'nail-biter'); avoids metronomic rhythm.
low severity: Strong emotional resonance mixed with technical detail; the narrative links the scientific mechanism (AI tech) directly to the humanitarian crisis (whale starvation) in a way that suggests human editorial intent.
low severity: Successfully integrates multiple specific, named experts (Zitterbart, McCauley, Slaathaug) and institutional bodies (WHOI, USCG, UCSB) and provides concrete, sourced statistics, indicating traditional reporting structure.
low severity: Specific scientific details (e.g., 2 degrees Celsius temperature difference, amphipods, precise whale population decline figures) are consistent with specialized knowledge and do not exhibit typical LLM confabulation patterns.
Human Indicators
The text features specific, verifiable quotes from named individuals (Daniel Zitterbart, Douglas McCauley, Josephine Slaathaug) and institutional entities, grounding the narrative in real-world reporting.
The integration of highly specific, technical data alongside deeply felt emotional appeals demonstrates a narrative structure typical of human journalistic storytelling rather than pure informational synthesis.
AI-powered whale-spotting tech may help save San Francisco Bay’s gray whales — Arc Codex