Analysis of the effect of El Niño and La Niña on Tecocomulco Lake, central basin, Mexico
Bibliographic record
Abstract
Tecocomulco Lake is a relic of the great basin of the central plateau of Mexico. Its surface area changes in response to both the variation in the inputs from inland areas and the effects of the climatic phenomena of El Niño and La Niña. It is endorreic, with a low rainfall and a low and intermittent fluvial input, a high evaporation, and a considerable influx of sediment due to deforestation and a bad management of the basin. The most important plant species is Schoenoplectus californicus that grows massively in muddy areas, decreasing the depth and reducing the flooded area. The lake is visited by birds from USA and Canada, that arrive to nest and reproduce. During the extremely dry years that coincide with the El Niño, approximately 9% of the dry surface is used by the local inhabitants as cropland, which generates a social problem during the extremely rainy years that coincide with the La Niña when the lake area increases and floods the cultivated land. In the 2001-2002 El Niño, the surface of Tecocomulco Lake decreased by 37% and the depth was 0.75-1 m. This may be associated with a higher temperature, and in consequence a high evaporation, a situation that requires corroboration through future studies.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".