GCOS EHI 1960-2020 Continental Heat Content
Bibliographic record
Abstract
Project: GCOS Earth Heat Inventory - A study under the Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory (EHI), and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period from 1960 to present. Summary: The file “GCOS_EHI_1960-2020_Continental_Heat_Content_data.nc” presents an updated estimate of the global continental heat storage for the period 1960-2020. For the first time, the continental heat storage is assessed as composed by: ground heat storage due to changes in subsurface temperatures, inland water heat storage due to the warming of inland water bodies, and permafrost heat storage due to thawing of ground ice in the Arctic. Furthermore, we argue that all three components of the continental heat storage should be monitored independently of their relative magnitude, as heat gain in the three components alters several important climate phenomena affecting society and ecosystems. This file contains the total continental heat storage relative to 1960. The ground heat storage has been estimated by inverting 1079 subsurface temperature profiles form the Xibalbá database (https://figshare.com/articles/dataset/Xibalb_Underground_Temperature_Database/13516487) and a bootstrap technique to aggregate the Singular Value Decomposition (SVD) inversions of each profile (Cuesta-Valero et al., 2022a). The data are used in Cuesta-Valero et al. (2022b) and von Schuckmann et al. (2022).
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.050 |
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".