Long-term ecological stability in high mountain lakes of Costa Rica
Bibliographic record
Abstract
Chirripó National Park (Costa Rica) contains an important mountain lake district that supplies water to downstream populations. Sediment records document remarkable stability in biotic assemblages over millennia, but the limnological response to the most recent decades of climate change is unknown. We assess ecological change in three lakes on the Chirripó massif by analyzing diatom assemblages in sediment cores spanning the past ∼100 years. Also, water chemistry samples and hourly depth-temperature profiles over a one-year period were compared to data from earlier studies. For all three study lakes, we recorded circumneutral, dilute, and oligotrophic conditions with near-continuous mixing water columns, matching earlier characterizations of the lakes from ∼50 years ago. The diatom profiles spanning the past century showed either stable assemblages or minor variations among the same dominant taxa. The lack of marked limnological and ecological change in the study lakes, compared to those documented in tropical mountain lakes globally, is explained by nearby meteorological records that show no significant warming trend over the past two decades and limited catchment disturbances from anthropogenic activities. These data qualify the study sites as “heritage lakes,” a concept developed to identify and protect rare aquatic ecosystems that are as close to pristine as possible.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".