
ALT Graph titled "Phone ban impact on student academic achievement." It shows estimated impacts on average grades across various groups. The x-axis represents the estimated impact in standard deviations, ranging from negative to positive values. The y-axis lists the categories: Prior GPA (Low, High), Grade level (High school, Three-year postsecondary, Postgraduate), and Field of study (Non-STEM, STEM). Data points include estimates and 95% confidence intervals. Overall effect is displayed at the top.
?' (08:19 PM), followed by 'Executed: addWFS('states', '[http://loca](https://pbs.twimg.com/media/HE0wrchXgAAeLvD.jpg?name=small&format=webp)
ALT Figure 6: Example of autonomous GIS operation where the fine-tuned SLM translates natural language queries into executable functions (addWFS and SetStrokeColor), updating the map visualization in real-time. The screenshot shows the AWebGIS App interface with three main sections: 1) A left sidebar containing 'Analysis Tools' and 'Layer Manager' panels, with options to 'Add WMS', 'Add WFS', 'Import', and 'Add Base', plus layer options for 'states', 'wfs', 'OpenStreetMap', and 'base'; 2) A central map display showing a base map of North America with the United States outlined in red stroke, including state boundaries and the Great Lakes region; 3) A right sidebar with 'Chat Assistant' and 'Activity Log' tabs, showing a conversation thread with timestamps. The chat displays natural language commands such as 'Can you add 'states' layer from [http://localhost:8080/geoserver/cite/wfs](http://localhost:8080/geoserver/cite/wfs)?' (08:19 PM), followed by 'Executed: addWFS('states', '[http://loca

ALT Figure 2: Conceptual workflow of the autonomous web-based geographical information systems (AWebGIS) approaches. The diagram shows a natural language query at the top splitting into two parallel processing paths. Approach I (Cloud-based LLMs), shown on the left in a dashed box, follows this sequence: 1) Natural-language query sent via HTTP to cloud API (OpenRouter to DeepSeek V3.1); 2) In the cloud, DeepSeek Chat V3.1 translates the query into a GIS function call using few-shot learning; 3) Predicted function call is returned as a text string to the web browser; 4) The JavaScript engine parses the function call and executes the corresponding GIS operation. Approach II (Browser-executable SLMs), shown on the right in a dashed box, follows this sequence: 1) Natural-language query processed locally through fine-tuned SLMs (e.g., T5-tiny); 2) In the user's web browser, an SLM translates the query into a GIS function call; 3) The JavaScript engine parses the function call and executes the c

































