Data on under-ice temperatures and solar radiation in Lake Ngoring (Qinghai-Tibet)
Bibliographic record
Abstract
Dataset description: Data on under-ice temperatures and solar radiation were collected in Lake Ngoring in winter 2015/2016 in a field experiment on under-ice mixing and radiation regime of Tibetan lakes. See Kirillin et al. (submitted to Geophysical Research Letters 2021) for the details on the experiment. _Metadata_ Study site: Lake Ngoring (35° 02.65'N, 97° 42.23'E) Observation period: 01 Nov 2015 - 01 Jun 2016 -- DATASET 1 Data type: Time series of water temperature [°C] at different depths. Probes: T-Solo (RBR Canada) Pre-processing: data sampled at 10 s rate and averaged over 30 min intervals. data file(s): TibetTemp.csv data format: comma separated variables data table contains temperature values (one column per measurement depth) except 1st column: UTC Date and Time [dd-mmm-yyyy HH:MM] 1st row: Depth from the surface [metres] ---- DATASET 2 Data type: quantum irradiance [μmol s^-1 m^-2] at 2.4 m and 3.6 m depth from the lake surface probe(s): JFE Advantech DEFI2-L cosine corrected PAR radiation logger (JFE Advantech, Japan) Pre-processing: data sampled at 10 min rate and averaged over 30 min intervals. data file(s): TibetRad.csv data format: comma separated variables 1st column: UTC Date and Time [dd-mmm-yyyy HH:MM] 2nd column: quantum irradiance [μmol s^-1 m^-2] at 2.4 m depth 3rd column: quantum irradiance [μmol s^-1 m^-2] at 3.6 m depth
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".