Special Issue of the Florida Geographer: The Halifax River Urban Watershed
Bibliographic record
Abstract
Globally, coastal urban areas are faced with numerous challenges that impact environmentaland community resilience, including saltwater intrusion into aquifers and drinking water sources,flooding, and pollutant contamination of major waterways. These and other similar threats areexpected to continue and worsen due to expanding urban development combined with the impactsof sea level rise. One such potentially impacted coastal area is the Halifax River (Figure 1), a 37-kmlong estuarine lagoon that runs along the eastern coast of Volusia County, Florida, with the city ofDaytona Beach located centrally along its banks. The municipalities along the Halifax River includethe coastal cities of Ormond Beach, Holly Hill, Daytona Beach, South Daytona, Port Orange, NewSmyrna Beach, Daytona Shores, and Ponce Inlet.Sustainability in its broad sense (ecological, social, economic, and technological) has beensuccessfully used as a central place-branding theme in a number of cities and regions throughout theworld. Coastal urban centers such as Daytona Beach could potentially benefit from preemptiverepositioning and rebranding that adapts local assets to local and global environmental trends in orderto promote sustainability/resilience while enhancing local economic activity and green tourism.In January 2019, Bethune-Cookman University (B-CU), Stetson University, and other regionalpartners initiated the Halifax River Urban Watershed Sustainability Initiative (HRUWSI) to betterunderstand and incorporate human dimensions and social factors into environmental research withthe goal of assisting in the development of a sustainable, resilient coastal community capable ofadaptation to changing coastal conditions. Thus far, the team produced a 23-minute documentary insummer 2019 that involved interviews of HRUW stakeholders, and a book titled The Halifax RiverUrban Watershed: A Holistic Approach to Sustainability (Cho et al. 2020).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.165 | 0.048 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".