Bibliographic record
Abstract
We have given ourselves the assignment of trying to write a book about places everyone sees, but no-one knows. In completing the work, we have tried to keep a number of things in mind. First, we recognize that readers have an insatiable curiosity for the truth, and within the context of natural history and ecology this is especially true because the answers to questions about non-human taxa sometimes help us interpret the significance of Homo to the world. This can comfort us. Second, we acknowledge the message that ‘complex questions have simple, easy to understand, wrong answers’. Thus, the kinds of simple questions we ask may not provide simple answers, and in the work that follows we will try to simplify only when such efforts can provide reasonably precise and accurate versions of the truth. Given that this is the first book on the topic of cliff ecology, it may also happen that certain topics have been so understudied that no effective summaries or syntheses can be made. When problems like this are encountered, we will try to bring them to the reader's attention. Lastly, we will try not to misrepresent to the reader the source of the motivation for doing science in general, and cliff ecology in particular – we love cliffs. Sometimes in the writing of science these motivations become lost in the intricacies of logic. You all know the wording: ‘In order to test whether species packing densities could be predicted from the equilibrium theory of island biogeography we sampled …’ which translates into English as ‘islands are fascinating.’
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.550 | 0.370 |
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".