Bryophytes as Indicators of Climate Change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Interest in climate change has increased tremendously in the past 10 to 15 yr, both within and outside the scientific community. The reason for this interest is directly related to the anticipated global warming that will result from increased concentrations of greenhouse gases in the atmosphere. As a result of this interest, several questions have been raised relative to climate warming. For example, how can we predict long term climatic change? How accurate are the predictions? What will be the severity and extent of the changes? How will biodiversity, ecosystems, and habitats be affected if climate change occurs as predicted? How long will it take for species and ecosystems to react to climate change? This essay will focus on the utilization of bryophytes to answer those questions. Bryophytes grow in almost all terrestrial and freshwater environments where plants can be found. These environments have a global distribution and are found in all climatic regimes with the exception of those on permanent ice. The success of bryophytes is largely due to their unique and very effective physiological water relation system that permits them to survive in the wide variety of climates in which they are found. This poikilohydric system permits them to grow during periods when water is available and to suspend their metabolism when water is lacking. Most genera are ectohydric and take up water through the whole surface of the plant and therefore do not need a root system to draw water from the soil. Also, nutrients are taken up through all surfaces from solutes in water that is in contact with the plants. As a result, bryophytes can grow on such very hard surfaces as rocks and tree trunks where higher plants cannot because their roots cannot penetrate the surface.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it