Bibliographic record
Abstract
While inaccessibility has protected cliffs from significant amounts of disturbance, it has also limited the amount of experimental work that deals with questions of the genesis and maintenance of cliff communities. Most previous studies have inferred mechanisms of community or ecosystem function from descriptions of the cliff biota. In this chapter, literature on the growth of individual species and populations, the establishment of patterns of relative abundance, and the development of species composition is briefly reviewed. Ideas about how physical factors influence the biota of cliffs are presented first, followed by a discussion of the control of communities through biotic interactions. Bedrock composition There are three aspects of geology and geomorphology that influence the biotic communities of cliffs: bedrock composition, structural heterogeneities, and erosion. Bedrock composition falls into three large categories: (1) hard siliceous rocks, mainly of igneous origin but also including some sedimentary rocks such as sandstones; (2) hard calcareous rocks, mainly of sedimentary origin but also including igneous or metamorphic rocks such as basalt and marble; (3) unconsolidated or indurated materials such as sand, gravel or loess. It is generally known that siliceous rocks produce acid soils that select for an array of plant species commonly called calcifuges (‘lime avoiders’). Conversely, calcareous rocks produce chalky soils with neutral to high pH values that select for a different array of plants known as calcicoles (‘lime seekers’) (Fitter & Hay, 1987). The low pH values of soils derived from acid rocks cause the accumulation of toxic levels of Fe 2+ /Fe 3+ or Al 3+ ions.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".