Baker Creek Research Catchment Hydrometeorological and Hydrological Data
Bibliographic record
Abstract
This record is for the dataset “ Baker Creek Research Catchment Hydrometeorological and Hydrological Data” at https://doi.org/10.20383/101.026 . It is uncommon to collect long term coordinated hydrometeorological and hydrological data in northern circumpolar regions. However, such datasets can be very valuable for engineering design, improving environmental prediction tools or detecting change. This dataset documents hydrometeorological and hydrological conditions in the Baker Creek Research Catchment from 2003 to 2016. Baker Creek drains water from 155 km2 of subarctic Canadian Shield terrain in Canada’s Northwest Territories. Seasonal half hourly hydrometeorological, ground temperature and soil moisture data were collected from representative locations, including exposed Precambrian bedrock ridges, peatlands, open black spruce forest and lakes. Hydrometeorological data includes incoming radiation, rainfall, temperature, humidity, winds, barometric pressure, and turbulent fluxes. Spring maximum snowpack water equivalent, depth and density data are included. Daily streamflow data are available from a series of nested watersheds ranging in size from 9 to 128 km2. These data provide the scientific and engineering communities with an opportunity to advance understanding of geophysical processes and improve infrastructure resiliency in this remote region. This dataset can be downloaded at: https://doi.org/10.20383/101.026
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.034 |
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".