Discover how SciArena is being used to evaluate foundation models’ capabilities in scientific literature tasks through community-driven, literature-grounded, and multi-disciplinary reasoning.

Eine aufregende Meldung sorgt in der deutschsprachigen Fan-Community für Begeisterung: Lee Junho könnte bald die Rolle einer Wassergottheit in einer übernatürlichen romantischen Komödie übernehmen! Am 12. Juni berichteten Medien, dass der Schauspieler die männliche Hauptrolle im kommenden Drama „Embassy for Foreign Monsters in Korea“ spielen werde. Die Agentur von Lee Junho, O3 Collective, bestätigte dazu: […]

The Boone County Council adopted Ordinance 2026-O3 to authorize taxable economic development notes to refund roughly $17 million of Anson TIF bonds, expecting about 2% interest savings and about $500,000 in extra TIF proceeds annually; a $1 million draw bond will be structured as a forgivable loan to Browning contingent on construction by Dec. 2030.

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Hi and welcome to the community! I’m sorry to say that the information shared by the other user above is incorrect and has no basis in reality. GPT-5, o3 and o3-pro use the same tokenizer. You can verify the actual token count with the tiktoken library available as an online tool. Regarding your question, I ran a quick test and can confirm there is no difference between o3 and o3-pro. A 1,000-token input for o3 is also a 1,000-token input for o3-pro. The same is true for GPT-5. I recommend double-checking that you are sending the exact same prompt and that no additional developer or system message is included in the API request.

M Praneeth presents their Google Summer of Code project on the immunization and vaccination schedule module for OpenMRS. This lightning talk covers work done and features of this important clinical tool. Presenter: M Praneeth Session Type: Lightning Talk Event: OpenMRS December 2025 Virtual Community Showcase - Day 2

Wildfires are widely known to increase atmospheric pollutant concentrations, deteriorating air quality. However, several studies have reported that wildfires can also reduce the concentrations of certain gaseous species. This study investigated the impacts of smoke plumes from wildfires that occurred in late July 2014 in Siberia, Russia, on downwind gaseous pollutants such as carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs), and ozone (O3). The Community Multi-scale Air Quality model was employed for air quality simulations, and wildfire emissions were obtained from the Fire INventory from the National Center for Atmospheric Research. Wildfire-induced CO, VOCs, and O3 were transported to Northeast China (NEC), the Yellow Sea (YS), and the Korean Peninsula (KP), resulting in elevated pollutant levels in these downwind regions. Interestingly, despite the transport of plumes, surface-level NOx concentrations over NEC, YS, and KP even decreased. Such reductions were not evident near the Siberian source areas but became apparent after the plumes reached the NEC region. In these regions, NO was converted through reactions with wildfire-induced VOCs (84.54%) and NO3 radicals (13.18%), while NO2 was primarily transformed into NO3 radicals via reactions with O3 (90.08%) or into organic nitrates through reactions with VOCs (9.92%). We found that the observed conversions in NOx concentrations were attributable to anthropogenic sources, as the conversion patterns varied depending on the level of anthropogenic NOx emissions in the sensitivity experiments. These results suggest that wildfire-driven NOx concentration changes in downwind regions cannot be fully explained by wildfire emissions alone. This study underscores the importance of accurately characterizing anthropogenic emission sources in regions affected by smoke plume transport to predict changes in gaseous pollutant concentrations following wildfire events. We expect that this study will enhance future wildfire-related research by improving the prediction of the spatial distribution and atmospheric behavior of gaseous pollutants.

Wildfires are widely known to increase atmospheric pollutant concentrations, deteriorating air quality. However, several studies have reported that wildfires can also reduce the concentrations of certain gaseous species. This study investigated the impacts of smoke plumes from wildfires that occurred in late July 2014 in Siberia, Russia, on downwind gaseous pollutants such as carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs), and ozone (O3). The Community Multi-scale Air Quality model was employed for air quality simulations, and wildfire emissions were obtained from the Fire INventory from the National Center for Atmospheric Research. Wildfire-induced CO, VOCs, and O3 were transported to Northeast China (NEC), the Yellow Sea (YS), and the Korean Peninsula (KP), resulting in elevated pollutant levels in these downwind regions. Interestingly, despite the transport of plumes, surface-level NOx concentrations over NEC, YS, and KP even decreased. Such reductions were not evident near the Siberian source areas but became apparent after the plumes reached the NEC region. In these regions, NO was converted through reactions with wildfire-induced VOCs (84.54%) and NO3 radicals (13.18%), while NO2 was primarily transformed into NO3 radicals via reactions with O3 (90.08%) or into organic nitrates through reactions with VOCs (9.92%). We found that the observed conversions in NOx concentrations were attributable to anthropogenic sources, as the conversion patterns varied depending on the level of anthropogenic NOx emissions in the sensitivity experiments. These results suggest that wildfire-driven NOx concentration changes in downwind regions cannot be fully explained by wildfire emissions alone. This study underscores the importance of accurately characterizing anthropogenic emission sources in regions affected by smoke plume transport to predict changes in gaseous pollutant concentrations following wildfire events. We expect that this study will enhance future wildfire-related research by improving the prediction of the spatial distribution and atmospheric behavior of gaseous pollutants.

Hi and welcome to the community! We have a different topic covering a related issue with container time-outs that may be helpful in this scenario. I will link it below. In short, the problem is that containers expire after 20 minutes and it’s an idea to call `retrieve()` every five minutes or so, to keep it alive. Here is the link with the full discussion: What is the best practice for keeping containers alive? API > Hi community I am using OpenAI’s Agent SDK and creating a code_interpreter tool or file_search bound to a container like this: # Create a container container = await openai_client.async_openai.containers.create(**container_params) tool_config = { "type": "code_interpreter", "container": container.id, } tool = CodeInterpreterTool(tool_config=tool_config) agent = Agent( name=f"Agent-{assistant.name}", instructions=instructions, model=openai_model, tools=[tool], mode…

Hi and welcome to the community! I don’t think o3-deep-research is the best fit, even if it could return structured outputs. You could use deep research to gather sources, then let another model generate the JSON you need. GPT-5-pro should handle that part well, though it is more expensive. Hope this helps!

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