Bibliographic record
Abstract
The artwork depicts the vivid colours of the lithium brine extraction ponds and the vulnerability of the ecosystems within northern Chile. Lithium extraction uses vast quantities of brine pumped from aquifers, reducing the water available to surrounding ecosystems. Mineral exploitation contributes to the depletion of the water resources of the hypersaline lagoons of the Salar de Atacama, endangering these rare ecosystems – and reducing the Artemia (brine shrimp) population, a food source for the flamingos. Two of most threatened species are the Andean and the James’ flamingos; both are endemic to the region. While the urgency of addressing climate change is undeniable, it is crucial to recognise that lithium extraction has an environmental cost in transitioning to a “greener” future. The artwork underscores the immediate need to find more sustainable ways to obtain lithium and consider alternative methods to brine evaporation to prevent further biodiversity loss. It also brings to the forefront the vital issues of Indigenous environmental justice and the concept of “other-than-human” challenging our traditional understanding of humanity's role in nature. Oil on canvas. Actual size: 28 x 36 cm. The artist does not use animal products or rare minerals to produce artwork. Updated with minor typographical corrections: June 30, 2025.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.052 | 0.011 |
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".