Bibliographic record
Abstract
In the six decades since Sir Arthur Tansley first coined the word ecosystem , an enormous amount of ecological research has been carried out in every imaginable habitat on earth. Forests, grasslands, deserts, tundra, wetlands and oceans have all been mapped in their distribution on the earth's surface and have revealed their structure and some aspects of their function to ecologists. Until recently, however, vertical cliffs have been almost completely overlooked as subjects for ecological study, even though some workers in Europe have included areas of steep rock in analyses of vegetation communities. For example, McVean and Ratcliffe (1962) described plant communities for the Scottish highlands but only a handful of stands had slopes greater than 60° and only one had a slope value of 80°. In other words, cliffs as defined in this book were not really included even if subsequent authors said that they were. McVean and Ratcliffe were also aware of the difficulty in dealing with cliff vegetation at the community scale. They stated: To many botanists this heterogeneous cliff vegetation is the most interesting of all but to the phytosociologist it is easily the most baffling. The larger, stable ledges usually bear tall herb communities and are amenable to the normal method of analysis but the open and patchy vegetation consisting of small herbs, sedges, grasses and bryophytes is very difficult to describe. … We have therefore analyzed only those cliff communities which provided stands of at least the normal minimal area of 2 × 2 m. … Description of the micro-communities naturally confined to open rocks is best reserved for detailed studies of individual rupestral species. (McVean & Ratcliffe, 1962, p. 88)
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.479 | 0.320 |
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".