ChatGPT for Science / academic researcher support from OpenAI

 If you received ChatGPT for Science / academic researcher support from OpenAI, I’d suggest wording the acknowledgment so it clearly identifies the support without implying that ChatGPT was an author.

The authors acknowledge support from OpenAI through the ChatGPT for Science program, which provided access to ChatGPT to support this research.

"Universities would prefer not to,"

 

In her essay "Universities would prefer not to," Hollis Robbins argues that higher education has become an institutional bottleneck slowing down society's transition into the AI era. Rather than leading or adapting to rapid technological change, universities are acting as a "brake on progress"—maintaining outdated degree monopolies, focusing on administrative survival and legacy curricular battles, and treating AI with caution or outright hostility.

Key takeaways supporting this message include:

• Dominance of Industry Over Academia: The vast majority of frontier AI developments (over 90% of notable models in 2025) and compute infrastructure come from private industry, leaving higher education far behind in driving AI innovation.
  
  
• Institutional Paralysis & Missed Potential: Most higher education institutions are in "AI paralysis," treating the defining technology of this era as something furtive or dishonest (focusing on cheat detection and traditional general education requirements) instead of completely redesigning curricula around AI-enabled knowledge production.
  
  
• The Shift in Knowledge Value: As basic "known knowns" (the base of the knowledge pyramid) become freely accessible via AI models, the true value of higher education lies in pushing human understanding upstream—teaching students and researchers to explore what AI doesn't yet know rather than testing them on static information transfer.

https://hollisrobbinsanecdotal.substack.com/p/universities-would-prefer-not-to?r=1h4jgq&utm_medium=ios&shareImageVariant=overlay&triedRedirect=true

wildbird, avian flu

 Here are the primary publicly available datasets and data streams that align directly with the domain, variables, and framework presented in this paper:

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1. Avian & Poultry Surveillance Datasets

• USDA APHIS Wild Bird Avian Influenza Surveillance Data

  • Provider: USDA Animal and Plant Health Inspection Service (APHIS)

  • Description: Official U.S. surveillance detections of High Pathogenicity Avian Influenza (HPAI) in wild birds. Tracks wild bird sampling events aggregated by county, state, detection date, virus subtype (e.g., H5N1 clade 2.3.4.4b), and host species.

• USDA APHIS Commercial and Backyard Poultry Outbreak Confirmations

  • Provider: USDA APHIS

  • Description: Records of confirmed HPAI cases in commercial poultry operations and backyard flocks across the U.S. Includes confirmation dates, state/county, flock type (e.g., turkey, egg-layer, broiler), and estimated flock numbers impacted.

• Open Avian Flu Dataset (OAFD)

  • Provider: Zenodo Open Repository

  • Description: A curated open dataset aggregating geographic, temporal, host species, and strain-level outbreak observations for global and national avian influenza tracking.

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2. Wildlife & Wild Bird Migration Datasets

• eBird Basic Dataset (EBD)

  • Provider: Cornell Lab of Ornithology

  • Description: Massive crowdsourced observational dataset tracking wild bird species occurrences, spatial distribution, relative abundance, and seasonal flyway migrations at high spatiotemporal resolution.

• USGS National Wildlife Health Center (NWHC) Wildlife Health Information Sharing Network (WHISPers)

  • Provider: U.S. Geological Survey

  • Description: Partner repository tracking wildlife mortality and disease events across North America, including waterbird and waterfowl morbidity/mortality surveillance.

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3. Environmental, Weather, & Agricultural Features

• PRISM Climate Data (PRISM Climate Group, Oregon State University)

  • Continuous spatial climate datasets covering temperature, precipitation, vapor pressure deficit, and dew point across U.S. counties for temporal risk modeling.

• USDA NASS Census of Agriculture & Cropland Data Layer (CDL) (USDA NASS)

  • High-resolution spatial land cover data, poultry density distributions, farm counts, and county-level agricultural inventory statistics.

• gNSSGO / SSURGO Soil & Terrain Data (USDA NRCS)

  • Soil characteristics, terrain slope, elevation, and hydrologic conditions across U.S. counties.