Determination of low bacterial concentrations in hyperarid Atacama soils: comparison of biochemical and microscopy methods with real-time quantitative PCR
Bibliographic record
Abstract
Hyperarid Atacama soils are reported to contain significantly reduced numbers of microbes per gram of soil relative to soils from other environments. Molecular methods have been used to evaluate microbial populations in hyperarid Atacama soils; however, conflicting results across the various studies, possibly caused by this low number of microorganisms and consequent biomass, suggest that knowledge of expected DNA concentrations in these soils becomes important to interpreting data from any method regarding microbial concentrations and diversity. In this paper we compare the number of bacteria per gram of Atacama Desert soils determined by real-time quantitative polymerase chain reaction with the number of bacteria estimated by the standard methods of phospholipids fatty acid analysis, adenine composition (determined by liquid chromatography - time-of-flight mass spectrometry), and SYBR-green microscopy. The number determined by real-time quantitative polymerase chain reaction as implemented in this study was several orders of magnitude lower than that determined by the other three methods and probably underestimates the concentrations of soil bacteria, most likely because of soil binding during the DNA extraction methods. However, the other methods very possibly overestimate the bacteria concentrations owing to desiccated, intact organisms, which would stain positive in microscopy and preserve both adenine and phospholipid fatty acid for the other methods.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".