DEVELOPING A QUANTITATIVE INDEX OF INTEGRITY AS A COMPREHENSIVE MEASURE IN ECOLOGICAL CHANGE ANALYSIS
Bibliographic record
Abstract
Ecological Integrity is one of the main scientific measures in the comprehensive assessment of ecosystems.The purpose of this study was to: (a) find a way to depict the disturbance gradient of our case study in northern Iran (b) develop an index of biotic integrity; (c) finally provide a baseline for assessing ecological integrity.Analytical metrics of spatial composition and configuration were applied to identify the disturbance gradient.Estimating of these metrics helped to define three levels of disturbance using the Ward's method of clustering analysis.A quantitative index of integrity was constructed, using three types of bird guilds including structural, functional and compositional .Results showed the range of integrity index at Miankaleh Peninsula was a value from 26 to 68.Statistical analysis including One-Way ANOVA and Pearson Correlation and paired sample t-test were conducted to investigate the validity and reliability of the Index.Findings of this research showed that biotic integrity in parts of the Miankaleh Peninsula was far from its intact condition.Developed index of biological integrity in this research can help to assess effects of the disturbance factors on the natural ecosystem of Miankaleh to prioritize the best management actions for restoring of this ecosystem.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".