Bibliographic record
Abstract
Abstract Evolving Tomorrow scrutinizes how evolutionary change happens, how modern-day genetic engineering can influence it, and what consequences of these forces we can expect to see in the future of the Anthropocene era. The first part of this book explores how living organisms change over time and how it can happen fast. It explains nature’s evolutionary forces, how these forces interplay with genetics, and the intricacies of genetic biotechnologies like CRISPR-Cas9 genome editing. Some genetic engineering with CRISPR-Cas9 gene drives will present a new force of evolution, termed genetic welding, if unleashed into the wild. Extensive real-world examples from nature draw on the author’s decades of experience conducting research around the world in the lab and in the field. The second part of the book investigates the applications and implications of manipulating evolution and genetic engineering to alter and create new species. It explores the ecosystem consequences and environmental bioethics of changes to plants and animals, including through de-extinction, rewilding, and invasive species. It deliberates competing perspectives of the meaning of wild and nature. It considers the ethical conundrums of genetic engineering of our own species, including the potential emergence of a new form of eugenics. The ideas developed in this book build the crucial links across evolution, ecology, biotechnology, and ethics to ground how global society will decide how and when it acts to shape the inevitable evolutionary changes that accumulate in the world of tomorrow and throughout the next millennia.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.004 |
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".