Mackenzie Delta Lake L520 thermistor and meteorological data 2022-2023, Canada
Bibliographic record
Abstract
Thermistor array, sediment temperature, and meteorological data for lake L520 ('Dock Lake') in the Mackenzie Delta near Inuvik, Northwest Territories, Canada. Data include in situ time series profiles of sediment temperature, and water column temperature, specific conductivity, dissolved oxygen, and water level. Sediment temperature data is from HOBO Onset UTBI-001 loggers. Temperature data is from RBR solo loggers. Specific conductance (SC) data is from Onset HOBO U24-001 loggers. Dissolved oxygen data is from PME miniDOT loggers. Water level data is from HOBO Onset U20L-04 loggers. Meteorological data is from a 2 meter (m) tall station deployed on a floating platform in the middle of the lake and includes: relative humidity and air temperature (Onset S-THC-M002), wind speed (Onset S-WSB-M003), wind direction (Onset S-WDA-M003), incoming shortwave radiation (Apogee pyranometer), and incoming longwave radiation (Apogee pyrgeometer). In-lake data is provided for three regions of the lake with differing depths along a horizontal transect: 2.1 m, 3.2 m, and 4.8 m depths. Data was collected over 2 separate periods: Aug 13, 2022 to Jun 4, 2023 for all three depths, as well as Jun 8 to Aug 4, 2023 for the 4.8 m depth. Meteorological data only collected during summer 2023. The purpose of this dataset is to support the examination of physical limnology in Arctic deltas.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.022 | 0.006 |
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".