Bibliographic record
Abstract
To a very great extent, cliffs are places that are of interest to everyone and no-one at the same time. This paradox attracts us and we think it will attract others when it becomes better known. People on all continents see images of cliffs in a wide variety of mass media and are consequently drawn as though pulled by a magnet to cliffs or habitats with extreme topography. Cliffs are sites with enormous spiritual value and may even be habitats that have given rise to a wide variety of our food plants, garden weeds and commensal animals. Yet these same sites have zero area when photographed from space, have attracted little scrutiny from scientists, and have received almost no legal protection from various forms of commercial exploitation. Some may be inclined to protest the last two statements based on the content of the book so far, but when one compares the vast and easily accessed literature for other habitat types, our conclusions are justified. The literature that we have reviewed and discussed in the preceding chapters almost always focuses on particular organisms, groups of organisms or specific aspects of cliff ecosystems without considering them as ‘places’ in the same way as lakes are considered as distinctive habitats by limnologists or forests by forest ecologists. A result of the particular organization that we have selected is that we may have reinforced rather than eliminated the idea of separate structures and functions on cliffs.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.151 | 0.039 |
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".