ECOSYSTEM MANAGEMENT AND MOOSE: CREATING A COHERENT CONCEPT WITH FUNCTIONAL MANAGEMENT STRATEGIES
Bibliographic record
Abstract
Ecosystem management is popular but poorly defined concept in conservation biology. Current vague, non-operational definitions provoke criticism of the concept and under- mine credibility of its associated principles. We propose definition of ecosystem management that emphasizes essential qualities of the concept rather than its accidental associations or properties, and that explains functional and operational attributes of ecosystem management rather than its descriptive characteristics. Based on these criteria, we offer definition of ecosystem management as a pattern of prescribed, goal-oriented environmental manipulations that: (1) treat specified ecological system of identifiable boundaries as the fundamental unit to be managed; (2) has, as its desired outcome, the achievement of state or collection of states in the ecosystem such that historical components, structure, function, products, and services of the ecosystem persist within biologically normal ranges and with normal rates of change; (3) uses naturally occurring, landscape- scale processes as the primary means of management; and (4) determines management objectives through cooperative decision-making of individuals and groups who reside in, administer, and/or have vested interests in the state of the ecosystem. Achieving workable ecosystem management is currently hindered by the lack of unified vision and system of values for ecosystems, the absence of permanent inter-agency bodies with authority to manage ecosystems across multiple jurisdic- tions, and the lack of administrative mechanisms for the translation of ecosystem research findings into ecosystem management policies. We propose strategies to overcome these obstacles and examine moose (Alces alces) as an example of species that is both important to ecosystem management and may benefit from it.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".