Hydroclimate reconstruction during the last 1000 years inferred by mineralogical and geochemical composition of a sediment core from Lake-Azuei (Haiti)
Bibliographic record
Abstract
Abstract. This study aims to reconstruct the hydro-climatic variations over the last 1000 yrs in Haiti using mineralogical and geochemical composition of well dated lacustrine sediment core retrieved from Lake Azuei. The results show changes in sedimentological processes linked to environmental and climatic variations. The general pattern suggests a wetter Medieval Climate Anomaly (MCA), drier Little Ice Age (LIA), high climate variability during the MCA-LIA transition and more anthropogenic impacts that dominate natural climate during the Current Warm Period (CWP). The MCA period (~1000–1100 CE) thus appears marked by increase sedimentation rate supported by higher terrigenous input linked to erosive events and consequently increases in precipitation. During the LIA, particularly from ~1450 CE to 1600 CE, there is a great variation towards a decrease of terrigenous input, which is related to a decrease on sedimentation rate and increase Mg-calcite precipitation, suggesting less precipitation and high evaporation respectively during dry climate conditions. The MCA-LIA transition (~1200–1400 CE) is characterized by variations between terrigenous input, Mg-calcite neoformation and organic matter deposition, which indicate succession of dry and humid conditions. The CWP (1800–2000 CE) shows a progressive increase on sedimentation rate and decrease of grey level, which indicate more organic matter sedimentation as consequence of anthropogenic activities in the surrounding basin of the lake. High-resolution grey level analysis, which reflects principally variations in terrigenous input, carbonate mineral neoformation and organic matter deposition, shows that the AMO, NAO, PDO and ENSO are the principal modes affecting the hydro-climatic changes in Haiti during the last millennium. In addition, temporal correlation of other Caribbean paleoclimate records with our geochemical and mineralogical data, suggests that trends observed in Lake Azuei were controlled by regional climate, likely associated with shifts in the position of the ITCZ.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".