Topsoil temperature data below different vegetation types at Trail Valley Creek, Canada, 2016-2018
Bibliographic record
Abstract
This dataset contains topsoil temperature data of 68 sensors, which were installed at the Arctic tundra site of Trail Valley Creek, Northwest Territories, Canada (133.499 °W, 68.742 °N). The sensors were located below six different vegetation types (trees, tall shrubs, riparian tall shrubs, dwarf shrubs, tussocks and lichen tundra) at a depth of 1 to 5 cm, protected from solar radiation. The sensors were placed between 0.22 m to more than 1 km apart from each other. The mean distance between nearest neighbouring sensors was 10.65 m. The sensor network is designed to monitor the effects of changing surface parameters such as vegetation, micro topography and soil moisture on seasonal thawing and freezing processes and on long-term warming of permafrost temperatures. The topsoil temperature was measured using coated iButton temperature loggers (DS1922L) at 0.0625°C resolution and an accuracy of 0.5° C (Maxim Integrated Products, Inc., 2015). The record covers the two periods from 28th of August 2016, 3:00 to 3rd of September 2017, 15:00 and from 4th of September 2017, 12:00 to 22nd of August 2018, 21:00 (UTC, local time + 7 hours) in 3 hourly resolution. Between the two periods, the sensors were removed, read out, and placed at the same positions again. The two periods were analysed by Grünberg et al., 2020, who describe the measurements in more detail.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".