Eco-Development Response to Climate Change and the Isostatic Uplift of Southwestern Finland
Bibliographic record
Abstract
To date, care for our planet is mainly focused on the remediation of climate change induced by the huge amount of anthropogenic emissions of greenhouse gasses and its precursors. Transforming fossil combustion to more sustainable energy worldwide is a wellknown example. In contrast, what is little known is that the environment shaped by humans is also challenged by relatively fast geological dynamical phenomena such as the isostatic uplift of Fennoscandia, parts of Canada and northwestern Russia. Due to this uplift, the archipelago along the coast of southwestern Finland and Sweden changes rapidly to mainland. This phenomenon deeply affects both nature as well as the environment, resulting in the relocation of human activities. Here, we interpret the on-ground observed regression of the Gulf of Bothnia on the coasts of southwestern Finland and its implications on countryside activities in the framework of the eco-development paradigm. Furthermore, remotely sensed data on surface wetness confirms this sea regression and the silting-up of the nearby lakes that drain precipitation to the Gulf. We show that this eco-development paradigm may rebalance nature, environment, humans and culture and that it is a valid alternative against the past and present-day socioeconomical approach that has accelerated the change in the Earth’s climate.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".