Wood traits of <i>Tectona grandis</i> during the 2015–2016 El Niño drought in the Amazon
Bibliographic record
Abstract
The Amazon biome is influenced by El Niño event, which reduce precipitation and increase temperature. However, little is known about its effects on tree formation dynamics in this region. Here, we evaluated the effects of local (precipitation, temperature, and solar insolation) and large-scale (El Niño) climatic variables on wood traits of Tectona grandis (teak) in the Amazon. Discs were collected from the base of trees aged 12 years and used for anatomical and physical analyses. We evaluated three periods (i.e., pre-El Niño (2012/2013), El Niño (2014/2015), and post-El Niño (2016/2017)). Wood density, vessels, and rays were compared to local and large-scale climatic variables. The extreme drought caused by El Niño event reduced the width and length of teak vessels. Additionally, precipitation during some months of the year increased vessel size and wood density. In some months of the year, ambient temperature reduced vessel width and length. Moreover, the effect of solar insolation depended on soil moisture availability. Thus, our results provided clear evidence of teak acclimatization to El Niño in the Amazon region and should promote further studies on tree responses to climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".