Carbon isotope discrimination in forest and pasture ecosystems of the Amazon Basin, Brazil
Bibliographic record
Abstract
Our objective was to measure the stable carbon isotope composition of leaf tissue and CO2 released by respiration (δr), and to use this information as an estimate of changes in ecosystem isotopic discrimination that occur in response to seasonal and interannual changes in environmental conditions, and land‐use change (forest‐pasture conversion). We made measurements in primary forest and pastures in the Amazon Basin of Brazil. At the Santarém forest site, δr values showed a seasonal cycle varying from less than −29‰ to approximately −26‰. The observed seasonal change in δr was correlated with variation in the observed monthly precipitation. In contrast, there was no significant seasonal variation in δr at the Manaus forest site (average δr approximately −28‰), consistent with a narrower range of variation in monthly precipitation than occurred in Santarém. Despite substantial (9‰) vertical variation in leaf δ13C, the average δr values observed for all forest sites were similar to the δ13C values of the most exposed sun foliage of the dominant tree species. This suggested that the major portion of recently respired carbon dioxide in these forests was metabolized carbohydrate fixed by the sun leaves at the top of the forest canopy. There was no significant seasonal variation observed in the δ13C values of leaf organic matter for the forest sites. We sampled in pastures dominated by the C4 grass, Brachiaria spp., which is planted after forest vegetation has been cleared. The carbon isotope ratio of respired CO2 in pastures was enriched in 13C by approximately 10‰ compared to forest ecosystems. A significant temporal change occurred in δr after the Manaus pasture was burned. Burning removed much of the encroaching C3 shrub vegetation and so allowed an increased dominance of the C4 pasture grass, which resulted in higher δr values.
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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 | 0.000 | 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".