Microplastics in the water: Indigenous storytelling as an educational method
Bibliographic record
Abstract
Western, euro-centric methodologies like the scientific method are essential for some forms of research, particularly for research in the biological realms of genetics and cell function. Using the scientific method by conducting a literature review and subsequently performing in silico experiments with simulation programs and machine learning, it has been concluded that microplastics in rivers, lakes and oceans are being consumed by aquatic wildlife, causing cellular and genetic damage, and biomagnifying up trophic levels, with evidence suggesting that microplastics have entered the human body. This is knowledge obtained by western research methods that would be relevant knowledge for Indigenous communities, based on the widespread Indigenous value that water is life. Based on the longknown Indigenous truth that Quanja Lake, and all the lakes on Manitoulin Island are connected to Lake Huron through underground channels, it is the responsible thing to inform communities of the dangers of microplastics that could be infiltrating the islands’ lakes via littering in Lake Huron from mainland Ontario. The task at hand was finding a way to deliver information obtained by western
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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".