Bibliographic record
Abstract
The number of species becoming extinct has drawn a significant deal of attention from scientists and non-scientists alike. This research reviews recent literature citing evidence for the impact humans have had on our planet and how our biological systems are affected in both known species of flora and fauna as well as unknown species of flora and fauna, the latter lacking documentation as well as sightings by humans. Theoretical research is derived from previous research investigating the impacts of humankind’s use of the land as well as population increases. Though there are many different definitions of what a mass extinction is and gradations of extinction intensity, a conservative approach is used to assess the seriousness of the current ongoing extinction crisis, setting the highest level of recognition for mass extinction, in extreme diversity loss associated with the Big Five extinction events (Barnosky, 2011). Understanding the relationship between extinction and functional diversity over time will be critical for making conservation work (Boyer & Jetz, 2014). If another mass extinction is allowed to progress, it would mean the end of biodiversity as we know it and would also mean that greater pressure would be placed on both humans and flora and fauna to survive in a world completely changed by the Anthropocene. Over the course of 8,000-10,000 years, humans grew in population and changed the landscape of the Earth (Foley, 2013). The research concludes that focus should be on preserving the environment and future research should be performed on the study of unknown species.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".