King-Higgins Project | Protecting our local Manatees
Bibliographic record
Abstract
Most of the Manatees that exist in the United States are found in the waters of Florida, and some call home right here in Volusia County. This beautiful animal however is among those on the endangered species list. Just recently in 2013, record number 829 deaths were reported, according to National Geographic and the Florida Fish and Wildlife Conservation Commission, and 317 in 2014. Most of these deaths can be attributed to pollution and injuries sustained by passing boats. However, there is a majority of these deaths that may be related to the high values of total N levels that are evident in the vast amounts of algae that have accumulated recently. These high levels of N come from sewage treatment centers, excess fertilizers, and high levels of human and animal waste. Natural levels of N exists in a balanced ecosystem, but these higher than normal values that enrich the local environment abnormally, known as eutrophication, can be extremely toxic to the manatees. The purpose of this project is to bring awareness to the local community of this issue by conducting cooperative research with Marine Science Center and displaying an informative awareness sign with the cooperation of the Halifax Harbor Marina.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.078 | 0.023 |
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".