Założenia koncepcji ekologicznego śladu i przykłady obliczeń dla dużych miast
Bibliographic record
Abstract
The article presents the concept of Ecological Footprint (EF), which is a quantitative indicator of human impact on the environment. The idea of EF has originated from the concept of carrying capacity. The Ecological Footprint measures how much of the land and water area a human population requires to produce the resource it consumes and to absorb its wastes, using the prevailing technology. The methodology was developed by Rees and Wackernagel (1996). The Ecological Footprint Assessment is a common supporting tool in planning and development of cities, subnational geographical regions and states. EF is important in ecological education at the primary and higher educational level, also including academic grade. At the beginning of the 21st century, requirements of the population in some countries (e.g. U.S., United Arab Emirates, Kuwait, Denmark, Australia, Canada) already exceed the planetary limits and ecological assets are becoming more critical. Implementation of the EF concept demands precise definition of many terms taken from ecology, geography, technology, or economy. The most important terms are explained in the glossary. More than half the global population (on average about 51%) live in cities (in Poland about62%). Their inhabitants have a substantial impact on the environment. The EF value for inhabitants of the capital city of Poland – Warsaw – in 2005 was 6.5 gha per capita, for the inhabitants of Cracow – 7.67 gha per capita. The average EF worldwide value in 2005 was approximately 2,1 gha per capita, and in 2007 1.8 gha per capita. The inhabitants of Warsaw and Cracow, through consumption of goods and services, exert significant pressure on the environment and aggravate the ecological deficit of the Earth.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".