Beyond MAGT: learning more from permafrost thermal monitoring data with additional metrics
Bibliographic record
Abstract
Abstract. Metrics such as the mean annual ground temperature (MAGT) and active layer thickness (ALT) are used to monitor and quantify permafrost change. However, these have limitations including those arising from the effects of latent heat, which reduce their sensitivity. We investigated the behaviour of existing and novel metrics derived from temperature observations (TSP metrics) using an ensemble of more than seventy 120-year simulations. We evaluated which TSP metrics provide new insight into permafrost change and evaluated how reliably each one indicates changes in sensible, latent, and total heat contents for different levels of sensor quality. We also quantified the effect of sensor placement on the magnitude of observed MAGT trends. Based on this, we recommend a parsimonious set of five TSP metrics that provide a better picture of permafrost thaw than MAGT alone. These are: height of the permafrost table (TOP), depth of zero annual amplitude (dza), thermal integral (τ), mean annual ground temperature (MAGT), and mean annual surface temperature (MAGST). We observed depth-related differences in MAGT warming rates of more than 0.23 °C dec-1 in 50 % of 10-year observation periods for observation depths between 10 m and 20 m. The magnitude of these differences roughly corresponds to the mean warming rate reported for discontinuous permafrost. The effect of sensor depth on warming trends is found to be greatest in ice-poor soils. These results illustrate the challenge of interpreting the magnitude of observed ground temperature trends. Our results can be used to inform permafrost monitoring strategies and help contextualize observed trends. Consistent metrics can be produced from observed and simulated thermal data via the "tspmetrics" library available on the Python Package Index (PyPi).
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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 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".