Bibliographic record
Abstract
Evidence-based research indicates that river basins are cleaner due to fewer people driving due to community lockdowns. Further, air quality has improved due to lessened home-to-work/school transportation and more Work-From-Home (WFH) remote options. Moreover, governments are experiencing challenges providing food to the most vulnerable communities from a food security standpoint. For example, those in global slums are particularly challenged during this time. Air, water, soil, and noise pollution have diminished since the pandemic as manufacturing production has been severely reduced in some industries. Food quality has been diminished because manufacturers are focused more on the quality of products rather than on perceived consumer quality. Solid and waste challenges abound as the use of hand sanitizers and chemicals to reduce the spread of the COVID-19 virus have created elevated levels of chemicals in waste programs threatening to refuse environmental factors and soil quality via toxic substances. The impacts of growth on the ecosystem are global because some species are in an overabundance within the food cycle, threatening the delicate balance of nature. On the other hand, algae overgrowth has lessened because of less carbon and nitrogen emissions. Also, human traffic and visitations to international parks have decreased. For instance, in Canada and other nature parks, animals on the plains now run free because of social distancing measures and park closures. From an economic perspective, as some industries have grown (masks/sanitation chemical/respirator production), others have declined (transportation/aviation). As a result, the pandemic has reduced air quality impacts of commercial aviation travel and lessened global cargo, which has reduced air and sound emissions worldwide
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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 teacher head, 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".