Assessing the Physical Ecological Impact (Footprint) of Industrial Development on Landscapes Beyond Protected Areas
Bibliographic record
Abstract
Abstract A tool to measure the potential ecological impact (footprint) of developments outside of protected areas is novel and long overdue. Whilst there are numerous methods currently available for mapping important regions for conservation within protected areas, there are few suitable tools available for assessing the ecological value of landscapes that are ‘beyond the reserves’. Given that this accounts for over 88% of the world's terrestrial surface, a systematic tool for determining the ecological value of these landscapes could be relevant to any industrial development that results in a parcel of land being transformed from ‘natural’ to ‘industrial’. Results are presented of a joint project between Statoil and University of Oxford, UK to develop an automatic web-based tool that can assess the ecological value of land outside of protected areas. Ecological factors currently considered within this tool include i) biodiversity, ii) vulnerability, iii) fragmentation, iv) connectivity and v) resilience. The tool has the capability to provide ecological valuations for parcels of land at 300m resolution and uses data that are publicly available and have mostly global coverage. We present results for three case study areas in Canada, Algeria and the Russian Federation to demonstrate its potential and how it can be used to plan development within a concession area in order that damage to local ecosystems and ecosystem functioning is minimized.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".