Breeding “CRISPR” Crops<a class="tippyShow" data-tippy-interactive="true" data-tippy-arrow="true" data-tippy-theme="light-border" style="cursor:pointer" data-tippy-content="<p style=text-indent:0in;>In loving memory of my beloved wife, Jean Georges.</p>"><sup>1</sup></a>
Bibliographic record
Abstract
The challenges which face the world today can be summed up in a few words: An increasingly congested world with dwindling areas of viable cultivated land and accelerating climate instability. The combined effect of these realities, together with the trend of striving to extend the average human life, puts the world on the path toward future catastrophe. This situation makes it imperative to seek realistic and practical solutions, which must be able to address food shortages and climate problems in a timely manner. In this article, an elucidative argument is presented with the intention of revealing the need for humanity to step back and consider more objectively the long-term benefits of crop-genome-editing for food security, looking beyond the unfounded negative notions about safety issues. If the faulty interpretations and arguments, which claim the CRISPR/Cas technology as being just another undesirable form of crop genetic modification stay unchallenged, they will continue to becloud the minds of decision makers and the public at large with inaccurate information. Eventually, the technology will be dismissed as a mere academic exercise with little or no benefit to future generations.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.148 | 0.093 |
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".