Wildlife and macrodebris pollution: examples from marine and freshwater environments
Bibliographic record
Abstract
Anthropogenic debris pollution is a problem affecting numerous wildlife species worldwide. Plastic, which makes up of most of the debris pollution found in natural areas, poses a threat to the survival of organisms primarily through entanglement and ingestion and ecological indicators species can play an important role in monitoring pollution levels in wildlife habitats. This portfolio examines anthropogenic debris pollution in two different environments: marine and freshwater. The marine environment is an important marine turtle nesting beach on the northeast coast of Costa Rica, Playa Norte, in the province of Limon. The specific objectives of the study in Playa Norte are to understand the characteristics (i.e.: type, size) of marine debris present in the beach, determine if there is a seasonal pattern of marine debris deposition and identify the possible sources of debris. The freshwater environment is Tommy Thompson Park in Toronto, Ontario, Canada. The study consists in assessing the incidence of anthropogenic debris in double-crested cormorant nests. Specifically, it examines the frequency and characteristics (i.e.: type, colour, size) of debris and how the frequency and type of debris compare to their surrounding environments (terrestrial and aquatic). The portfolio closes up with a reflection on the role of environmental education in addressing the problem of plastic pollution through education and awareness. My objective is to highlight the urgent need to address anthropogenic debris pollution as a pressing environmental concern that impacts marine and freshwater ecosystems alike, as well as to be able to provide solutions and recommendations for monitoring and mitigating this issue in important wildlife habitats.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".