Bibliographic record
Abstract
Cliffs are largely inaccessible to people and their livestock and are therefore generally free from disturbances such as grazing and fire. As we have already shown, cliffs cannot support organisms with high productivity and therefore most vegetation on cliffs is small and unassuming. We believe that these features are the reasons why cliffs have attracted far less attention from biologists than other more accessible habitats with large numbers of productive macroscopic organisms. Maycock and Fahselt (1992) studied the vegetation of high arctic cliff faces and scree slopes in Canada that had previously been described as ‘unvegetated’. On these surfaces they found 156 plant species, of which half were lichens, one-quarter were macroscopic higher plants and one-quarter were bryophytes. The authors offer no satisfactory explanation as to why others might have so grossly misrepresented the diversity of species in these habitats, but they hint that the appearance of low productivity has discouraged close scrutiny in the past. The same suggestion was also offered by Larson (1990) to explain the lack of prior discovery of an ancient forest of stunted Thuja occidentalis on the apparently bare cliffs of the bare-looking Niagara Escarpment in southern Ontario, Canada. The small size of many cliffs often results in them being viewed as ‘break-points’ or transition-points in landscapes, rather than as separate landscape elements. This view leads to the characterization of cliffs as the ‘edges’ of other places, rather than places in their own right. We feel that all of these factors help to explain the small amount of scientific literature dealing with the vegetation of cliffs compared to the vast amount of literature on level-ground forests, grasslands, wetlands, deserts, and tundras of the world.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.108 | 0.038 |
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".