Bibliographic record
Abstract
The Impact Measurement and Conservation System (IMACS) was developed to calculate environmental and human condition impacts and to apply conservation required to neutralize such impacts for products and services purchased by end-user consumers (1- 6). With its implementation, the IMACS system would allow the fastest return to the best approximation of pre-industrial sustainable conditions (Global warming reversal and wildlife area restoration). All environmental impacts take place on a location. Location Based Impacts (LBIs) are environmental impacts assigned to parcels (land) or designated areas (marine). Under IMACS, LBIs are distributed in a dynamic fashion over the products made and services rendered using these areas. This article focuses on the use of remote sensing instruments systems used to accurately measure the underlying variables needed for parcel and designated area delineation and the environmental impacts taking place on them (LBIs). These impacts include landscape change and the subsequent use as cultivated area, changes in biodiversity, greenhouse gas emissions, fresh water consumption, soil and surface water acidification, soil & sediment loss, coastal area at risk of flooding, atmospheric ozone layer damage and includes all applicable types of conserving impacts, including wildlife area conservation, carbon storage and protection of coastal areas from flooding due to sea level rise. Using currently available remote sensing technology and after training using ground data, area mapping, parcel delineation and the measurement of most environmental impact variables can be done using satellites, by using aerial sensors or by using combinations. Implementation of IMACS requires the development of data products that combine remote sensing based environmental data with civic databases (users of parcel and designated areas), allowing easy, automated and low-cost extraction of LBI data.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".