Tracking Open Versus Closed‐Canopy Boreal Forest Using the Geochemistry of Lake Sediment Deposits
Bibliographic record
Abstract
Abstract Identifying geochemical paleo‐proxies of vegetation type in watersheds could become a powerful tool for paleoecological studies of ecosystem dynamics, particularly when commonly used proxies, such as pollen grains, are not suitable. In order to identify new paleological proxies to distinguish ecosystem types in lake records, we investigated the differences in the sediment geochemistry of lakes surrounded by two boreal forest ecosystems dominated by the same tree species: closed‐canopy black spruce‐moss forests (MF) and open‐canopy black spruce‐lichen woodlands (LW). This study was designed as a first calibration step between terrestrial modern soils and lacustrine sediments (0–1000 cal yr BP) on six lake watersheds. In a previous study, differences in the physical and geochemical properties of forest soils had been observed between these two modern ecosystems. Here we show that the geochemical properties of the sediments varied between the six lakes studied. While we did not identify geochemical indicators that could solely distinguish both ecosystem types in modern sediments, we observed intriguing differences in concentrations of C:N ratio, carbon isotopic ratio, and aluminum oxide species, and in the stabilization of their geochemical properties with depth. The C accumulation rates at millennial scale were significantly higher in MF watersheds than in LW watersheds. We suggest that these variations could result from organic matter inflows that fluctuate depending on forest density and ground vegetation cover. Further investigations on these highlighted geochemistry markers need to be performed to confirm whether they can be used to detect shifts in vegetation conditions that have occurred in the past.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".