Assessing range-wide “contribution to recovery” by multiple local governments for a threatened species
Bibliographic record
Abstract
To recover a threatened or endangered species, numerous local government jurisdictions are usually involved in habitat mitigation and conservation planning actions for evaluating impacts to habitat.In the USA local governments make official land use decisions.A social-ecological case study of multiple counties is presented tabulating the relative "contribution to recovery" by each county for giant garter snake (GGS; Thamnophis gigas), a federally and state-listed threatened California endemic watersnake species that is reliant on rice agriculture.The entire geographic range of the GGS is examined in relation to multiple county boundaries, recovery unit boundaries, federal habitat conservation plan (HCP) coverage, California natural community conservation plan (NCCP) coverage, and piecemeal mitigation (areas lacking formal conservation plans).Results indicate that of the 22 counties that cover the range of the GGS, nine counties have HCPs that cover the species in 38% of the range and of those nine HCPs six have NCCPs covering 14% of the range.Thus, more than half of the range (62%) mitigates for impacts to the GGS in a project-by-project (piecemeal) manner with no HCP, while 24% of the range has a population jeopardy standard covered by HCPs and 14% has a population recovery standard covered by NCCPs.However, four of the nine recovery units are substantially covered by HCP or NCCP conservation plans (~65-81%), while the remaining five units have far less coverage (~1-36%).Ninety-nine percent of all known GGS occurrences were found in Sutter, Sacramento, Yolo, Colusa, Butte, Merced, Glenn, San Joaquin, Fresno, Solano, and Kern counties (n = 85, 55, 51, 44, 36, 27, 17, 9, 9, 4, 4, respectively).These 11 counties will play an important role toward contributing to recovery of the GGS.In theory, the variation in different conservation standards over a species' range could have significant implications for its ultimate recovery potential.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".