Relevance and present limits of space applications for evaluation of potentials
Bibliographic record
Abstract
Satellites offer synoptic views of the physical phenomena of interest to renewable energies. Satellite images are routinely used to map solar radiation. Wave height over the whole ocean can be imaged, and maps can be constructed which may be helpful to assess the swell close to seashore. Scatterometers aboard satellite provide maps of wind speed offshore. Sea surface temperature is mapped from space for many decades. Using such maps and solar irradiation and wind speed as inputs to a model yields vertical profile of the temperature, from bottom to surface, helpful to assess the potentail of a OTEC system. Satellite data can be assimilated into numerical models. They can be fused with in situ measurements, e.g. buoys, to produce maps of greater accuracy, such as for sea surface temperature. They can be fused with other satellite data having different properties. For example, scatterometers and SARs both provide wind speed but with different spatial resolution and time scale. Wind statistics obtained from scatterometers far from the coast can be enhanced in spatial resolution and brought to the coast by a fusion with SAR images.
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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.008 |
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".