Effects of Altered Temperature and Precipitation on Desert Protozoa Associated with Biological Soil Crusts
Bibliographic record
Abstract
Biological soil crusts are diverse assemblages of bacteria, cyanobacteria, algae, fungi, lichens, and mosses that cover much of arid land soils. The objective of this study was to quantify protozoa associated with biological soil crusts and test the response of protozoa to increased temperature and precipitation as is predicted by some global climate models. Protozoa were more abundant when associated with cyanobacteria/lichen crusts than with cyanobacteria crusts alone. Amoebae, flagellates, and ciliates originating from the Colorado Plateau desert (cool desert, primarily winter precipitation) declined 50-, 10-, and 100-fold, respectively, when moved in field mesocosms to the Chihuahuan Desert (hot desert, primarily summer rain). However, this was not observed in protozoa collected from the Chihuahuan Desert and moved to the Sonoran desert (hot desert, also summer rain, but warmer than Chihuahuan Desert). Protozoa in culture began to encyst at 37 degrees C. Cysts survived the upper end of daily temperatures (37-55 degrees C), and could be stimulated to excyst if temperatures were reduced to 15 degrees C or lower. Results from this study suggest that cool desert protozoa are influenced negatively by increased summer precipitation during excessive summer temperatures, and that desert protozoa may be adapted to a specific desert's temperature and precipitation regime.
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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 | 0.000 | 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) | 0.000 | 0.000 |
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".