Desenvolupament d’un mètode d’odometria visual per a un robot mòbil en entorns exteriors
Bibliographic record
Abstract
For many regions of the world, current climate change projections are only available at coarser spatial resolution from Global Climate Models (GCMs) that cannot directly be used in impact assessment and adaptation studies at regional and local scale. Impact assessment studies require high-resolution climate data to drive impact assessment models. To overcome this data challenge, we produced a station based climate projection (precipitation and maximum and minimum temperature) for Ethiopia, Kenya, and Tanzania using observed daily data from 211 stations obtained from the National Meteorological Agency of Ethiopia and international databases. Moreover, 26 large-scale climate variables derived from the National Centers for Environmental Prediction reanalysis data (1961-2005) and second generation Canadian Earth System Model (CanESM2, 1961-2100) are used. Statistical Down-Scaling Model (SDSM) is used to produce the required high-resolution climate projection by developing a statistical relationship between the large- and local-scale climate variables. The predictors are analysed more than 16458 times and we provided 20 ensembles for the current (1961-2005) and future (2006-2100, under RCP2.6, RCP4.5, and RCP8.5) climate.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".