Derelict Boats in Florida Harbors Pose Environmental Threats
Bibliographic record
Abstract
Over six hundred derelict boats are removed from docks across Florida each year as these vessels remain a persistent issue. Since the 2008 recession, the number of abandoned and decrepit vessels has steadily increased and recently worsened in the wake of 2020 pandemic-induced economic downturn. Through analyzing journal articles, academic databases, and local news, these vessels pose numerous negative environmental and social effects. These findings suggest that there needs to be a better way to remove these boats and a much more thorough investigation into who owns these vessels, in order for the owner to pay the fee rather than loyal taxpayers. These boats leak gasoline, about 20-30% of their fuel capacity, which enter the waterways, causing harm to wildlife and other boaters. These half-sunken vessels are not easily visible, and mariners end up driving over them by accident, causing damage and endangering passengers. In the past year, fifteen boats have been removed from the Halifax River, costing taxpayers over $10,000 per boat. Removing abandoned boats is a large and complicated expense with removal costs up to $40,000 for boats left out during hurricanes. This cost burdens taxpayers, as they fund companies to remove the vessels. The escalating number of boats requiring removal along with rising removal expenses poses challenges for communities state-wide. Implementing change necessitates enhanced surveillance at docks and increased boater awareness of these vessels. Conducting further research will determine the effects of these unattended boats on a nationwide scale, ensuring safety of public health and the ocean.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".