Water quality in The Lachuá Ecoregion Landscape: Comparing streams from Forest, Milpa, and an Oil Palm plantation
Bibliographic record
Abstract
The hydrological network in the Lachuá Ecoregion (EL), Alta Verapaz, Guatemala, hosts high levels of biodiversity and supplies water to 44 Mayan communities. Despite this critical fact, this network has been threatened by scarcely monitored industrial activities including the rapidly expanding oil palm monoculture (Elaeis guineensis Jacq). Regardless of Lachua’s freshwaters importance, there is little information on how this monoculture impacts them. We compared water-quality properties from streams in oil palm plantations (P), paddock and milpa systems (M), and primary forests (F) in EL. During 2015-2016, 13 rivers were sampled (5 times) for water temperature, pH, dissolved oxygen (DO), conductivity, hardness, chemical and biochemical oxygen demand (COD and BOD), and concentration of silica, nitrates, phosphates, and ammonia. Several parameters showed significant differences. P were 2.7ºC and 1.8ºC warmer than M and F and carried 1.4mg/L more nitrates than F. F carried 10.8mg/L and 11.8mg/L more silica than M and P. M showed intermediate temperatures and silica concentrations, as well as 14.8µS/cm and 8.9µS/cm lower conductivities than P and F. Additionally, COD in M was 9.9mg/L and 4.6mg/L lower than P and F. We believe higher temperatures and lower silica in P are due to the loss of riparian forest and their role in buffering temperatures and recycling silicon. In addition, the existence of secondary forest (guamil) in M might explain the intermediate temperatures and silica concentrations. Our results highlight the contributions of forests to waterways and suggest potential water-quality depletion from the oil palm expansion in EL.
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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 | 0.001 | 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".