Bibliographic record
Abstract
Cumulative impact mapping is a spatially-explicit, semi-quantitative method useful for illustrating the relative cumulative effects of human activities on marine habitats over broad spatial scales. Cumulative impact mapping uses a relatively simple model that identifies areas where activities and habitats intersect in space, then applies a vulnerability weight to determine an impact score for each activity-habitat intersection. The cumulative impact score is the sum of all habitat-activity intersections within a unit area. The method requires three data sources: 1) spatial distribution of marine habitat classes (e.g., beach intertidal, shallow pelagic, and seagrass beds), 2) spatial distribution and relative intensity of human activities (e.g., fishing, shipping, and industrial sites) and their associated stressors (e.g., sedimentation and noise), and 3) a matrix of vulnerability scores to quantify the relative impact of each stressor on each habitat class. Habitat classes encompass intertidal, subtidal, shelf, and deep ecosystems, and include inorganic substrates and biogenic features in each. Pelagic ecosystems are also included, separated into shallow and deep classes. Between Pacific and Atlantic coasts, the division of habitat classes is similar, although the depth ranges vary slightly. Human activities/stressors are separated into four data types: land-based, coastal, marine, and fishing. Human activities/stressors are represented as a relative intensity value, which depends on the nature of the activity or stressor, the way they may interact with the habitat, and data availability. Vulnerability matrices for use in the Canadian Pacific and Atlantic regions were adapted from existing vulnerability matrices developed previously for the California Current (Pacific) and Massachusetts coast (Atlantic). Regional ecosystem experts were surveyed for their expert opinion on vulnerability scores and rankings for all habitat-stressor combinations relevant to each coast. Previously generated vulnerability matrix scores were updated in the associated Research Document (Clarke Murray et al 2023) based on expert review, and recommendations were provided for activity/stressor rankings per habitat class. In Pacific region, 120 (12%) habitat stressor scores were increased and 26 (3%) were reduced based on expert feedback. For the Atlantic matrix, 105 (11%) and 90 (9%) habitat-stressor scores were increased or decreased, respectively. New activities/stressors were also recommended for consideration but not reviewed and could be updated in the future. Efforts to update vulnerability scores through elicitation of regional habitat experts was supported as an improvement to previous applications of the Halpern method. Future enhancements could include reviewing vulnerability scores across habitat types with stressor experts (e.g., experts on the impacts of fishing). Cumulative impact mapping has a number of potential uses: to inform and assess future environmental change or marine spatial planning scenarios (e.g., to assess planned development and climate change), to inform strategic or regional environmental assessment, to identify areas for field research and investigation, or to prioritize activities or stressors for management or mitigation action. Its specific use within the Canadian marine spatial planning context is still to be determined. In addition to the resulting maps, these high-quality spatially-explicit marine data and knowledge products generated as individual components during the cumulative impact mapping process can benefit planners, stakeholders, and other scientists engaged in Marine Spatial Planning and ecosystem-based management, including conservation planning. Given this is a data-driven approach, with outputs that represents a snap-shot in time, the quality and age of the data inputs will impact its usefulness for planning. Sensitivity analyses, e.g., further investigating the impact of changing vulnerability scores on overall results, may help to improve confidence in the results.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.071 | 0.011 |
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".