Modelling tree-ring cellulose δ <sup>18</sup> O variations of two temperature-sensitive tree species from North and South America
Bibliographic record
Abstract
Abstract. Oxygen isotopes in tree-rings (δ18OTR) are widely used to reconstruct past climates. However, the complexity of climatic and biological processes controlling isotopic fractionation is not yet fully understood. Here, we use the MAIDENiso model to decipher the variability of δ18OTR of two temperature-sensitive species of relevant paleoclimatological interest (Picea mariana and Nothofagus pumilio) and growing at cold high-latitudes in North and South America. In this first modelling study on δ18OTR values in both northeastern Canada (53.86° N) and western Argentina (41.10° S), we specifically aim at: (1) evaluating the predictive skill of MAIDENiso to simulate δ18OTR values, (2) identifying the physical processes controlling δ18OTR by mechanistic modelling and, (3) defining the origin of the temperature signal recorded in the two species. Although the linear regression models used here to predict daily δ18O of precipitation (δ18OP) may need to be improved in the future, the resulting daily δ18OP values adequately reproduce observed (from weather stations) and simulated (by global circulation model) δ18OP series. The δ18OTR values of the two species are correctly simulated using the δ18OP estimation as MAIDENiso input, although some offset in mean δ18OTR levels is observed for the South American site. For both species, the variability of δ18OTR series is more likely linked to the effect of temperature on isotopic enrichment of the leaf water rather than on the isotopic composition of the source water. We show that MAIDENiso is a powerful tool for investigating isotopic fractionation processes but that the lack of a denser isotope-enabled monitoring network recording oxygen fractionation in the soil-vegetation-atmosphere compartments limits our capacity to decipher the processes at play. This study proves that the eco-physiological modelling of δ18OTR values is necessary to interpret the recorded climate signal more reliably.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".