Human impacts and Anthropocene environmental change at Lake Kutubu, a Ramsar wetland in Papua New Guinea
Bibliographic record
Abstract
The impacts of human-induced environmental change that characterize the Anthropocene are not felt equally across the globe. In the tropics, the potential for the sudden collapse of ecosystems in response to multiple interacting pressures has been of increasing concern in ecological and conservation research. The tropical ecosystems of Papua New Guinea are areas of diverse rainforest flora and fauna, inhabited by human populations that are equally diverse, both culturally and linguistically. These people and the ecosystems they rely on are being put under increasing pressure from mineral resource extraction, population growth, land clearing, invasive species, and novel pollutants. This study details the last ∼90 y of impacts on ecosystem dynamics in one of the most biologically diverse, yet poorly understood, tropical wetland ecosystems of the region. The lake is listed as a Ramsar wetland of international importance, yet, since initial European contact in the 1930s and the opening of mineral resource extraction facilities in the 1990s, there has been a dramatic increase in deforestation and an influx of people to the area. Using multiproxy paleoenvironmental records from lake sediments, we show how these anthropogenic impacts have transformed Lake Kutubu. The recent collapse of algal communities represents an ecological tipping point that is likely to have ongoing repercussions for this important wetland's ecosystems. We argue that the incorporation of an adequate historical perspective into models for wetland management and conservation is critical in understanding how to mitigate the impacts of ecological catastrophes such as biodiversity loss.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".