Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Marco Polo, Christopher Columbus and Captain James Cook were explorers who left their mark in history, not only because they travelled into unknown territories but also because they returned home to write about their fascinating adventures. They faced many obstacles and not infrequently suffered from lack of support. Despite all encumbrances, they laboured on because they believed that what they were doing was important and potentially rewarding. Their stubbornness and perseverance led them to great discoveries, if not always the rewards. Likewise, in this book we bring to you a collection of 44 'adventures', each written by scientists who journeyed into the unknown domains of biological control. They made their way towards a goal and, in doing so, faced numerous and unexpected hurdles, which had to be addressed for them to complete their objectives. The field of biological control encompasses not only biology but, as we will learn, also dozens of other disciplines and human activities, including technology, art, business, psychology, economics, law, international trade, sociology and many more. The chapters presented here illustrate that one needs to master combinations of all these elements in order to deploy a successful biological control programme.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it