Origin and dynamics of soil organic matter and vegetation changes during the Holocene in a forest-savanna transition zone, Brazilian Amazon region
Bibliographic record
Abstract
Carbon isotope data on soil organic matter (SOM) were collected along an ecosystem transect 90 m in length that includes a tropical forest on the plateau, a transitional forest-savanna and savanna in a depression. Total organic carbon data show a significant increase in carbon content from sites representing forest ecosystem to sites representing savanna ecosystem. It was hypothesized that carbon accumulation in the depression is controlled by flooding conditions that slow down carbon decomposition and in part by carbon transport from the upper part of the transect (the savanna and the transition forest-savanna areas) into the depressions by water during the rainy season. The origin of the carbon was confirmed by using soil 13 C analysis. The savanna sites located in the depression showed δ 13C values between –19.5‰ and –22.5‰ indicating a mixture of C3 and C4 plants. The vegetation cover in the depression is predominantly C3 grasses with d 13C values of about –27‰ and –26‰. In the site under savanna located at an elevation slightly higher, the d 13C value was more enriched (–16‰) showing the predominance of C4 plants (d 13C of –13.6‰). At the forest-savanna transition and in the forest ecosystem the d 13C values were characteristic of C3 plants (–25‰ and –28.1‰). 14C and 13C data indicate that the organic matter of mixed origin has been deposited for at least the last 7000 years in the savanna depressions. The 13C pattern observed in the soil organic matter profiles indicate a predominance of C3 plants in the early part of the Holocene. About 7000 to 4000 years ago, the data show the influence of C4 plants, indicating forest regression associated with a drier climate than at present. The more recent 13 C records suggest forest expansion, and the return to a climate similar to the present.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".