Long‐term water dynamics in the Lascaux cave karst: Impact of tree removal
Bibliographic record
Abstract
Abstract Quantifying the hydrological dynamics of unsaturated karst environments in relation to their native vegetation is a critical step in developing adaptation strategies. This study investigates the effect of tree removal on water dynamics in the Lascaux karst hill in southwestern France. Monthly electrical resistivity tomography surveys taken over a decade evaluated rock mass heterogeneity and water content. The influence of felled tree water use on the upper 1.2 m of rock mass was studied by assessing species‐level sap flow measurements scaled to estimate site transpiration. Seasonal changes in resistivity ranged from 100 to 300 Ω·m, with peak values during summer and minima in winter. Interannual variability was notable, with the resistivity recorded in summer 2021 decreasing to 130 Ω·m due to above‐average precipitation. The removal of 10% of the trees in November 2016 is followed by a 27% reduction in resistivity (up to 90 Ω·m) with strongest effects during summer. During the summer immediately following tree felling, the effect was even observed down to a depth of 5 m. More globally, water flow in the emergence airlock in Lascaux cave increased by 74 m 3 annually after the trees had been felled, which is in the same order of magnitude as the 37 m 3 reduction in water use estimated from sap flow measurement from the nine trees cut just above the cave. The study showed a direct interplay between vegetation and hydrological processes within karst environments and highlighted the importance of considering tree management when analyzing water storage reservoirs under 1 m below ground.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".