Impacts on wave-driven harbour agitation due to climate change in Catalan ports
Bibliographic record
Abstract
Abstract. The objective of the present work is to analyse how changes in wave patterns due to the effect of climate change can affect harbour agitation (oscillations within the port due to wind waves). The study focuses on 13 harbours located on the Catalan coast (NW Mediterranean) using a methodology with general applicability. To obtain the patterns of agitation, a Boussinesq-type model is used, which is forced at the boundaries by present/future offshore wave conditions extracted from recently developed high-resolution wave projections in the NW Mediterranean. These wave projections were obtained with the SWAN model forced by present/future surface wind fields projected, respectively, by five different combinations of global and regional circulation models (GCMs and RCMs) for the A1B scenario. The results show a general slight reduction in the annual average agitation for most of the ports, except for the northernmost and southernmost areas of the region, where a slight increase is obtained. A seasonal analysis reveals that the tendency to decrease is accentuated in winter. However, the inter-model variability is large for both the winter and the annual analysis. Conversely, a general increase with a larger agreement among models is found during summer, which is the period with greater activity in most of the studied ports (marinas). A qualitative assessment of the factors of variability seems to indicate that the choice of GCM tends to affect the spatial pattern, whereas the choice of RCM induces a more homogeneous bias over the regional domain.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".