Local Geomorphological Potentialities for the Use of Wave Energy in Mexican Coasts: a Recognition with Google Earth
Bibliographic record
Abstract
of high scientific-applied commitment, which in the case of extensive national territories such as the Mexican case, can only be resolved in a first approach and definition, with the use of satellite images or with the use of a sophisticated spatial analysis platform, as offered and guaranteed by Google Earth. With these premises and for a first look at the potential conditions of Mexican localities for the location of engineering prototypes for the use of wave energy, a satellite observation and assessment was carried out along the coasts of the country, in order to identify sites with geologic-geotechnical and geomorphological conditions potentially suitable for these purposes. The reconnaissance was centered on the location of abrasive coasts, undeniably modeled by wave dominance, and taking into consideration the proximity of coastal settlements, which could benefit from the conversion of wave energy into electrical microgeneration. Due to the extension of its coasts, Mexico occupies the third place in America, after the United States of America and Canada, but despite this geographical dimension of its coastal system, not all of its coastlines offer optimal conditions for the assimilation and conversion of wave energy, sometimes because the oceanographic and wave-generating wind regime does not have the frequency, annual permanence, fetch and power to guarantee permanent and efficient flows of energy and, in others, because the concentrations of wave energy, reflected in the abrasive geomorphic modeling of the coast, are not enough to guarantee the suitability of the sites. The satellite recognition of the Mexican coasts, through the platform of Google Earth, allowed to identify coastal sectors and localities, under a macro-location perspective according to territorial planning, which will reinforce previous decisions in future projects of location of suitable sites. These first attempts made it possible to recognize 147 of these coastal sectors and sites: Veracruz (7), Quintana Roo (2), Oaxaca (22), Guerrero (15), Michoacan (12), Colima (4), Jalisco (15), Nayarit (6), Sinaloa (3), Sonora (5), Southern Baja California (20) and Baja California (36). The vast majority of these sectors have small coastal towns as heirs to the conversion of wave energy to electricity. Keywords: suitable sites, geological-geomorphological conditions, satellite images, Google Earth, Mexico.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".