Getting cold feet: tree productivity at the mercy of soil temperature
Bibliographic record
Abstract
This scientific commentary refers to `Negative effects of low root temperatures on water and carbon relations in temperate tree seedlings assessed by dual isotopic labelling' by Wang and Hoch (doi: 10.1093/treephys/tpac005). In his pioneering 1868 Lehrbuch der Botanik (Textbook on Botany) Julius Sachs, often considered to be the father of plant physiology (Kutschera and Niklas 2018), noted that ‘the absorption of water through the roots is also confined to certain limits of temperature... Tobacco plant and Gourd [sic] no longer absorb sufficient water to replace a small loss by evaporation in a moist soil of from 3 to 5 °C’ (Sachs 1868). At the time, Sachs also noted that these low temperatures are limiting to other processes in plants such as the growth of green tissue or the exchange of oxygen and carbon dioxide. With considerable hindsight, we now know that water uptake is inextricably linked to turgor pressure and thus essential for cell expansion (Lockhart 1965). As such, turgor is a major limiting factor in tree growth and scaling-up its effects on forest biomass production is key to carbon sink and climate modeling (Friedlingstein et al. 2020, Cabon and Anderegg 2022). Yet, global models still overwhelmingly rely on ambient air rather than soil temperatures for their modeling even though soils show negative temperature offsets from recorded air temperatures from April to August in boreal and temperate zones, and nearly year round in the tropical forested regions of the globe (Lembrechts et al. 2022). These differences are highly dependent on both anthropogenic land use and climate-driven changes in ground cover (Lembrechts and Nijs 2020). Clearly, more attention needs to be given to the effects of low soil temperatures on plant roots and how they may impact these tree productivity models and, thus, projected climate change simulations.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".