Data from: Mapping the spatial heterogeneity of watershed ecosystems and water quality in rainforest fjordlands
Bibliographic record
Abstract
This dataset is used in a research paper by Giesbrecht et. al. (2025) in the journal Ecosystems with the title "Mapping the spatial heterogeneity of watershed ecosystems and water quality in rainforest fjordlands". https://doi.org/10.1007/s10021-025-00964-x In this study, we examined spatial controls on the quality of freshwater exported from diverse watersheds in fjordlands of a coastal temperate rainforest. Samples were collected about once per month for a year from the outlets of 56 watersheds spanning from high mountains with icefields to low islands with extensive wetlands. Watershed size ranged from < 1.5 km2 to 5,782 km2 (Homathko River), yet in the regional and global context, all are considered “small” coastal watersheds (< 10,000 km2 following Milliman and Syvitski, 1992). The study watersheds were spatially distributed along two fjordland transects on the south-central coast of British Columbia, Canada (51°57' N to 50°07' N and 128°09' W to 123°44' W).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| 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 teacher head, 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".