Modeling and mapping in support of the Regional conservational Strategy Framework
Bibliographic record
Abstract
Prior to November 2010, when The Intertwine Alliance launched the Regional Conservation Strategy (RCS) and Biodiversity Guide (RBG) efforts for the Portland-Vancouver metropolitan region, conservation priorities in the metropolitan region were identified at a broad regional scale that generally excluded urban areas (e.g., state conservation strategies and Willamette Synthesis); were regional but based solely on expert opinion (e.g., Natural Features); and consisted of localized priorities that abruptly ended at jurisdiction boundaries. The goal of the RCS was to fill in the gaps between broad and local scales of information related to conservation priorities. RCS members envisioned a data-driven approach that could add a regional perspective to local efforts and facilitate cross-scale cooperation toward protecting remaining valuable habitat in the Portland-Vancouver metropolitan region. Also, RCS members expected that the product would complement rather than replace local knowledge, by validating what we know and expanding to areas we know less well. In June 2011, INR completed an initial proof-of-concept product describing high value conservation areas in the Portland-Vancouver region. The product demonstrated a methodology that enabled stakeholder involvement while also being data-driven. In September 2012, we completed a second version of this product that is reported on in this document. While the product is considered complete at this time, it is expected and hoped that the models and data will be updated and improved upon into the future as more and better information becomes available so that the product functions as a "living work" rather than a one-time snapshot in time. Several key products resulted from the project: the High Value Habitat data describing high value terrestrial habitat within the metropolitan region, the Riparian Habitat data describing high value habitat adjacent to streams and rivers, and the high spatial resolution land cover data set describing land cover at a 5 m spatial resolution.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".