Teck's recent experience in pursuing Net Positive Impact (NPI) for biodiversity at coal mines in BC and Alberta
Bibliographic record
Abstract
In 2011, Teck Coal Limited (Teck) adopted an aspirational, long-term (2030) goal to achieve a Net Positive Impact (NPI) on biodiversity. This paper provides an overview of conceptual and technical advances as they relate to our NPI strategy and targets. Key learnings include: (i) The scope of our biodiversity commitment has proven possible to operationalize and has been generally supported by our communities of interest and First Nations (ii) Through the use of historical aerial photos and data, digital imagery, and predictive ecosystem mapping, we have developed credible pre-development baselines of ecosystems and wildlife habitat suitability for our operations, even though some are many decades old (iii) In order to support a quantitative accounting of our gains and losses to ecosystems, we have developed a measurement framework for assessing the condition or quality of ecosystems based on the BC provincial Biogeoclimatic Ecosystem Classification (BEC) and database of benchmark data. We still face some challenges, including the lack of a landscape conservation plan for the Elk Valley region and knowledge of specific reclamation techniques that will allow us to restore the full range of ecosystems that existed at our operations prior to disturbance.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".