Bibliographic record
Abstract
Humans in contact with less-than-favourable environments are constantly exerting control over those natural realms to make them more hospitable and functional for our everyday life. A prime example of such an exertion, and the focus of this research, is that of salting our sidewalks and roads to make transit safer for both pedestrians and vehicles. However, as is common with many anthropogenic practices, when out of sight we often pay little thought to the consequences of this action, and the potential environmental harm is effectively out of mind. Accordingly, the objective of our research project is to observe how ecosystems are being affected by road salt and its effect on local flora and fauna. This is a local issue that could effectively be extrapolated to a more global scale.The planned approach was to first do an exhaustive search for the current literature on this topic, diving into the primary literature, effectively bringing researchers up to speed on the science behind this issue. Next we wanted to contact local authorities, such as the City of Kingston to understand more about local salting initiatives. Finally based on what we know about both the local situation and potentially harmful effects that salting our roads may have on the environment, formulate a set of recommendations to improve both the salting and the surrounding natural environment.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".