The effects of Hurricane Otto on the soil ecosystems of three forest types in the Northern Zone of Costa Rica
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Hurricanes rapidly deposit large amounts of canopy material onto tropical forest floors, stimulating metabolic processes involved in the decomposition of these materials and production of N and C resources into the food web. However, little is known about the effects that hurricanes have on specific soil microbial taxa or functional groups involved in these processes. The objectives of this study were to determine how Hurricane Otto influenced three different tropical forest soil ecosystems within the first 8 months after causing damage to a tropical forest by assessing the soil C and N factors and how the soil bacterial and fungal community compositions differed before and after the hurricane. Soil samples were collected from five 2000 m 2 permanent plots in Lowland, Upland and Riparian forest systems within the same area in the Northern Zone of Costa Rica. Standard methods were used to measure the amounts Total N, NO 3 - , NH 4 + , Total organic C, and Biomass C, while Illumina MiSeq methods were used to generate bacterial and fungal sequences. All data were analyzed using univariate and multivariate statistical methods. Using this “before and after” study design, it was found that the levels of the inorganic N and Biomass C were greater in the Post-Hurricane soil samples. The mean proportion of DNA sequences of complex C degrading/lignin degrading, NH 4 + -producing, and ammonium oxidizing bacteria, and the complex C degrading/wood rot/lignin degrading and ectomycorrhizal fungi also were greater in the Post-Hurricane soils. We also provide evidence that the excessive amounts of canopy leaf litter and woody debris deposited on the forest floor during Hurricane Otto appears to be selecting for genera that become more dominant Post-Hurricane, perhaps because they may be better able to rapidly process the newly deposited C and N-rich canopy material. This is a rare “before and after” a natural hurricane design that warrants further monitoring.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it