Como avaliar o impacto positivo líquido em biodiversidade: uma análise crítica de métricas
Bibliographic record
Abstract
The pressure on natural environments due to industrial development is a reality worldwide and grows with the increasing demand for the consumption of material goods, fuel, energy, food, and housing. Human intervention in the environment causes impacts on biodiversity, which is fundamentally important for the maintenance of ecological processes and is directly related to the environmental quality of tropical ecosystems. To ensure no net loss in biodiversity, when these impacts cannot be avoided, even when reduced, they should ideally be compensated. The United Nations (UN) with the Sustainable Development Goals (SDGs) has a specific agenda for biodiversity. The determination of performance standards applied to projects financed by institutions associated with the World Bank, in consortium with the International Finance Corporation (IFC), has resulted in commitments from various segments of the industrial sector to ensure no net loss in biodiversity. The Kunming-Montreal agreement signed at the 15th meeting of the Conference of the Parties (COP15) in December 2022 establishes the protection of 30% of the planet by 2030 aiming at the protection of biodiversity. The need to compensate for damages and consequently impacts caused by the loss of biodiversity, in the face of industrial development, as well as to prove the additionality of conservation projects for biodiversity, demands a scientific basis for establishing technical criteria developed to quantify and measure these losses and gains. There are several publications that propose to calculate losses and project gains with the purpose of compensating biodiversity in an equivalent way using metrics. To date, there has been no systematic review or comparative classification of available biodiversity accounting alternatives that aim to facilitate the selection of metrics for application in tropical forests. This study aimed to carry out the survey and critical analysis of metrics used to determine net losses and gains in biodiversity, with a view to selecting methods 11 to evaluate biodiversity performance for the industrial sector. A total of 20 metrics were considered in the analysis, considering advantages, disadvantages, complexity, effectiveness, readiness, type of biodiversity, among other aspects arranged in a decision matrix (ANNEX 1), which resulted in the recommendation to use 5 metrics that can express net losses and gains in biodiversity applicable to tropical forests. The selected metrics were: Biodiversity Significance Index (BSI), Biodiversity Metric 3.0 (Defra), Net Impact Assessment (WBCSD/CSI), Loss Gain Calculator and the Disaggregated Model.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".